diff --git a/.all-contributorsrc b/.all-contributorsrc index eef8a5aa16..ed984ed15b 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -674,6 +674,27 @@ "ideas", "bug" ] + }, + { + "login": "bobonice", + "name": "bobonice", + "avatar_url": "https://avatars.githubusercontent.com/u/22030806?v=4", + "profile": "https://github.com/bobonice", + "contributions": [ + "bug", + "code" + ] + }, + { + "login": "kratman", + "name": "Eric G. Kratz", + "avatar_url": "https://avatars.githubusercontent.com/u/10170302?v=4", + "profile": "https://github.com/kratman", + "contributions": [ + "doc", + "infra", + "bug" + ] } ], "contributorsPerLine": 7, diff --git a/.github/release_checklist.md b/.github/release_checklist.md deleted file mode 100644 index f461d471e0..0000000000 --- a/.github/release_checklist.md +++ /dev/null @@ -1,14 +0,0 @@ -- Run `scripts/update_version.py` to - - - Increment version number in - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json`, and check if any links fail - - - Update baseline of registries in `vcpkg-configuration.json` as the latest commit id from [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) - - Update `CHANGELOG.md` with a summary of the release - -- Update jax and jaxlib to latest version in `pybamm.util` and fix any bugs that arise -- If building wheels on Windows gives a `vcpkg` related error - revert the baseline of default-registry to a stable commit in `vcpkg-configuration.json` diff --git a/.github/release_reminder.md b/.github/release_reminder.md new file mode 100644 index 0000000000..2515166837 --- /dev/null +++ b/.github/release_reminder.md @@ -0,0 +1,9 @@ +--- +title: Create {{ date | date('YY.MM') }} (final or rc0) release +--- +Quarterly reminder to create a - + +1. pre-release if the month has just started. +2. non-pre-release if the month is about to end (**before the end of the month**). + +See [Release Workflow](./release_workflow.md) for more information. diff --git a/.github/release_workflow.md b/.github/release_workflow.md new file mode 100644 index 0000000000..04f0667773 --- /dev/null +++ b/.github/release_workflow.md @@ -0,0 +1,74 @@ +# Release workflow + +This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers. + +## rc0 releases (automated) + +1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files - + + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/_static/versions.json` + - `CHANGELOG.md` + + These changes will be automatically pushed to a new branch `YY.MM`. + +2. Create a new GitHub _pre-release_ with the tag `YY.MMrc0` from the `YY.MM` branch and a description copied from `CHANGELOG.md`. + +3. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. + +## rcX releases (manual) + +If a new release candidate is required after the release of `rc0` - + +1. Fix a bug in `YY.MM` (no new features should be added to `YY.MM` once `rc0` is released) and `develop` individually. + +2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...). + +3. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - + + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/_static/versions.json` + - `CHANGELOG.md` + + These changes will be automatically pushed to the existing branch `YY.MM`. + +4. Create a new GitHub _pre-release_ with the same tag (`YY.MMrcX`) from the `YY.MM` branch and a description copied from `CHANGELOG.md`. + +5. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. + +## Actual release (manual) + +Once satisfied with the release candidates - + +1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release. + +2. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - + + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/_static/versions.json` + - `CHANGELOG.md` + + These changes will be automatically pushed to the existing branch `YY.MM`. + +3. Next, a PR from `YY.MM` to `main` will be generated that should be merged once all the tests pass. + +4. Create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. + +5. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. + +## Other checks + +Some other essential things to check throughout the release process - + +- If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id. +- Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise +- Make sure the URLs in `docs/_static/versions.json` are valid diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml deleted file mode 100644 index 8d029d3bc5..0000000000 --- a/.github/workflows/create_release.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Create GitHub release - -on: - push: - branches: main - workflow_dispatch: - -jobs: - create-release: - # This workflow is only of value to PyBaMM and would always be skipped in forks - if: github.repository_owner == 'pybamm-team' - runs-on: ubuntu-latest - permissions: - contents: write - strategy: - matrix: - python-version: [3.8] - - steps: - - uses: actions/checkout@v4 - - - name: Get current date - run: | - echo "VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV - echo "TODAY=$(date +'%d')" >> $GITHUB_ENV - - - name: Fail the job if date < 20 - if: env.TODAY < 20 - uses: actions/github-script@v6 - with: - script: core.setFailed('This workflow should be triggered only at the end of the month, or else it will create a release for the wrong month.') - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install dependencies - run: | - pip install wheel - pip install --editable . - - - name: Get Changelog - run: python -c "from scripts.update_version import get_changelog; get_changelog()" - - - name: Create release - uses: softprops/action-gh-release@v1 - with: - tag_name: ${{ env.VERSION }} - body_path: CHANGELOG.md diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml index d727ca0784..4282b8f83d 100644 --- a/.github/workflows/lychee_url_checker.yml +++ b/.github/workflows/lychee_url_checker.yml @@ -45,6 +45,7 @@ jobs: --accept 200,429 --exclude-path ./CHANGELOG.md --exclude-path ./scripts/update_version.py + --exclude-path docs/conf.py './**/*.rst' './**/*.md' './**/*.py' diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index 54dd70d5a7..919a00d6ef 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -1,18 +1,18 @@ name: Build and publish package to PyPI on: - push: - branches: main + release: + types: [published] workflow_dispatch: inputs: target: description: 'Deployment target. Can be "pypi" or "testpypi"' default: "pypi" debug_enabled: - type: boolean - description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' - required: false - default: false + type: boolean + description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' + required: false + default: false jobs: build_windows_wheels: @@ -130,15 +130,12 @@ jobs: build_sdist: name: Build sdist runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: - python-version: ${{ matrix.python-version }} + python-version: 3.8 - name: Install dependencies run: pip install wheel diff --git a/.github/workflows/release_reminder.yml b/.github/workflows/release_reminder.yml new file mode 100644 index 0000000000..204ace0b68 --- /dev/null +++ b/.github/workflows/release_reminder.yml @@ -0,0 +1,21 @@ +name: Create a release reminder + +on: + schedule: + # Run at 10 am UTC on days-of-month 1 and 28 in January, May, and September. + - cron: "0 10 1,28 1,5,9 *" + +permissions: + contents: read + issues: write + +jobs: + remind: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: JasonEtco/create-an-issue@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + filename: .github/release_reminder.md diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 7488c40aa2..472de06f0e 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -2,51 +2,73 @@ name: Update version on: workflow_dispatch: + inputs: + append_to_tag: + description: 'Leave blank for an actual release or "rc1", "rc2", ..., for release candidates."' + default: "" + schedule: + # Run at 10 am UTC on day-of-month 1 in January, May, and September. + - cron: "0 10 1 1,5,9 *" jobs: update-version: # This workflow is only of value to PyBaMM and would always be skipped in forks if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] steps: + - name: Get current date for the first release candidate + if: github.event_name == 'schedule' + run: | + echo "VERSION=$(date +'v%y.%-m')rc0" >> $GITHUB_ENV + echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + + - name: Get current date for a manual release + if: github.event_name == 'workflow_dispatch' + run: | + echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV + echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + + - uses: actions/checkout@v4 + if: github.event_name == 'schedule' + with: + ref: 'develop' + - uses: actions/checkout@v4 + if: github.event_name == 'workflow_dispatch' + with: + ref: '${{ env.NON_RC_VERSION }}' - - name: Set up Python ${{ matrix.python-version }} + - name: Set up Python uses: actions/setup-python@v4 with: - python-version: ${{ matrix.python-version }} + python-version: 3.8 - name: Install dependencies run: | pip install wheel - pip install --editable . - - - name: Get current date - run: echo "VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + pip install --editable ".[all]" - name: Update version run: python scripts/update_version.py - - name: Create Pull Request - id: version_pr - uses: peter-evans/create-pull-request@v5 + - uses: EndBug/add-and-commit@v9 + if: github.event_name == 'schedule' with: - delete-branch: true - branch-suffix: short-commit-hash - commit-message: Update version to ${{ env.VERSION }} - title: Update to ${{ env.VERSION }} - body: | - - [x] Update to ${{ env.VERSION }} - - [ ] Check the [release checklist](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_checklist.md) - - - name: Make a PR from develop to main + message: 'Bump to ${{ env.VERSION }}' + new_branch: '${{ env.NON_RC_VERSION }}' + + - uses: EndBug/add-and-commit@v9 + if: github.event_name == 'workflow_dispatch' + with: + message: 'Bump to ${{ env.VERSION }}' + + - name: Make a PR from ${{ env.NON_RC_VERSION }} to main + if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc') uses: repo-sync/pull-request@v2 with: + source_branch: '${{ env.NON_RC_VERSION }}' destination_branch: "main" - pr_title: "Make release ${{ env.VERSION }}" - pr_body: "**DO NOT MERGE UNTIL #${{ steps.version_pr.outputs.pull-request-number }} IS MERGED.**

Make release ${{ env.VERSION }}" + pr_title: "Make release ${{ env.NON_RC_VERSION }}" + pr_body: "**Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)**" github_token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 288f139afa..61a585178b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.0.288" + rev: "v0.0.290" hooks: - id: ruff args: [--fix, --ignore=E741, --exclude=__init__.py] diff --git a/CHANGELOG.md b/CHANGELOG.md index 87dd894812..cff4551ef8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,30 +1,40 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) ## Features - - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237)) - Processed variables now get the spatial variables automatically, allowing plotting of more generic models ([#3234](https://github.com/pybamm-team/PyBaMM/pull/3234)) - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205)) +- Implement the MSMR model ([#3116](https://github.com/pybamm-team/PyBaMM/pull/3116)) ## Bug fixes - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330)) +- Fixed a bug that caused incorrect results of β€œ{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)). - Fixed a bug that occured in `check_ys_are_not_too_large` when trying to reference `y-slice` where the referenced variable was not a `pybamm.StateVector` ([#3313](https://github.com/pybamm-team/PyBaMM/pull/3313) - Fixed a bug with `_Heaviside._evaluate_for_shape` which meant some expressions involving heaviside function and subtractions did not work ([#3306](https://github.com/pybamm-team/PyBaMM/pull/3306)) +- Fixed bug causing incorrect activation energies using `create_from_bpx()` ([#3242](https://github.com/pybamm-team/PyBaMM/pull/3242)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) - Fixed a bug where the "basic" lithium-ion models gave incorrect results when using nonlinear particle diffusivity ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207)) - Particle size distributions now work with SPMe and NewmanTobias models ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207)) - Fix to simulate c_rate steps with drive cycles ([#3186](https://github.com/pybamm-team/PyBaMM/pull/3186)) -- Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019. -- Error generated when invalid parameter values are passed. ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132)) -- Thevenin() model is now constructed with standard variables: `Time [s], Time [min], Time [h]` ([#3143](https://github.com/pybamm-team/PyBaMM/pull/3143)) +- Always save last cycle in experiment, to fix issues with `starting_solution` and `last_state` ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177)) +- Fix simulations with `starting_solution` to work with `start_time` experiments ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177)) - Fix SEI Example Notebook ([#3166](https://github.com/pybamm-team/PyBaMM/pull/3166)) +- Thevenin() model is now constructed with standard variables: `Time [s]`, `Time [min]`, `Time [h]` ([#3143](https://github.com/pybamm-team/PyBaMM/pull/3143)) +- Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132)) +- Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096)) + +## Optimizations + +- Improved how steps are processed in simulations to reduce memory usage ([#3261](https://github.com/pybamm-team/PyBaMM/pull/3261)) +- Added parameter list support to JAX solver, permitting multithreading / GPU execution ([#3121](https://github.com/pybamm-team/PyBaMM/pull/3121)) ## Breaking changes - The class `pybamm.thermal.OneDimensionalX` has been moved to `pybamm.thermal.pouch_cell.OneDimensionalX` to reflect the fact that the model formulation implicitly assumes a pouch cell geometry ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) - The "lumped" thermal option now always used the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" to compute the cell cooling regardless of the chosen "cell geometry" option. The user must now specify the correct values for these parameters instead of them being calculated based on e.g. a pouch cell. An `OptionWarning` is raised to let users know to update their parameters ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) +- Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205)) - Added option to use an empirical hysteresis model for the diffusivity and exchange-current density ([#3194](https://github.com/pybamm-team/PyBaMM/pull/3194)) - Double-layer capacity can now be provided as a function of temperature ([#3174](https://github.com/pybamm-team/PyBaMM/pull/3174)) - `pybamm_install_jax` is deprecated. It is now replaced with `pip install pybamm[jax]` ([#3163](https://github.com/pybamm-team/PyBaMM/pull/3163)) diff --git a/README.md b/README.md index 1b54201f5b..1d92b4c357 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) -[![All Contributors](https://img.shields.io/badge/all_contributors-61-orange.svg)](#-contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-63-orange.svg)](#-contributors) @@ -263,6 +263,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Siegel
Jason Siegel
πŸ’» πŸ€” Tom Maull
Tom Maull
πŸ’» ⚠️ ejfdickinson
ejfdickinson
πŸ€” πŸ› + bobonice
bobonice
πŸ› πŸ’» + Eric G. Kratz
Eric G. Kratz
πŸ“– πŸš‡ πŸ› diff --git a/docs/conf.py b/docs/conf.py index d0b1683aff..1cb1a521ae 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -303,14 +303,52 @@ # a conflict with the sphinx-docsearch extension for Algolia search nbsphinx_requirejs_path = "" + +# For notebook downloads (23.5 onwards), we get the version from the environment +# variable READTHEDOCS_VERSION and set it accordingly. + +# If the version is set to "latest", then we are on the develop branch, and we +# point to the notebook in the develop blob +# If we are on "stable", we point to the notebook in the relevant release tree +# for the PyBaMM version +# On a PR build, we use READTHEDOCS_GIT_COMMIT_HASH which will always point to changes +# made to a notebook, if any. +# On local builds, the version is not set, so we use "latest". + +if (os.environ.get("READTHEDOCS_VERSION") == "latest") or ( + os.environ.get("READTHEDOCS_VERSION") is None +): + notebooks_version = "develop" + append_to_url = f"blob/{notebooks_version}" + +if os.environ.get("READTHEDOCS_VERSION") == "stable": + notebooks_version = version + append_to_url = f"tree/v{notebooks_version}" + +if os.environ.get("READTHEDOCS_VERSION_TYPE") == "external": + notebooks_version = os.environ.get("READTHEDOCS_GIT_COMMIT_HASH") + append_to_url = f"blob/{notebooks_version}" + +github_download_url = f"https://github.com/pybamm-team/PyBaMM/{append_to_url}" +google_colab_url = github_download_url.replace("github.com", "githubtocolab.com") + +html_context.update( + { + "notebooks_version": notebooks_version, + "github_download_url": github_download_url, + "google_colab_url": google_colab_url, + } +) + nbsphinx_prolog = r""" {% set github_docname = 'github/pybamm-team/pybamm/blob/develop/docs/' + env.doc2path(env.docname, base=None) %} -{% set readthedocs_download_url = -'https://docs.pybamm.org/en/latest/' %} +{% set notebooks_version = env.config.html_context.notebooks_version %} +{% set github_download_url = env.config.html_context.github_download_url %} +{% set google_colab_url = env.config.html_context.google_colab_url %} {% set doc_path = env.doc2path(env.docname, base=None) %} @@ -323,7 +361,7 @@

An interactive online version of this notebook is available, which can be accessed via - Open this notebook in Google Colab @@ -331,7 +369,7 @@

Alternatively, you may - download this notebook and run it offline.

diff --git a/docs/source/api/models/lithium_ion/electrode_soh.rst b/docs/source/api/models/lithium_ion/electrode_soh.rst index 8942b2394e..4bf7d57dbe 100644 --- a/docs/source/api/models/lithium_ion/electrode_soh.rst +++ b/docs/source/api/models/lithium_ion/electrode_soh.rst @@ -8,4 +8,8 @@ Electrode SOH models .. autofunction:: pybamm.lithium_ion.get_min_max_stoichiometries +.. autofunction:: pybamm.lithium_ion.get_initial_ocps + +.. autofunction:: pybamm.lithium_ion.get_min_max_ocps + .. footbibliography:: diff --git a/docs/source/api/models/lithium_ion/index.rst b/docs/source/api/models/lithium_ion/index.rst index f925d2c3d4..1a72c3c662 100644 --- a/docs/source/api/models/lithium_ion/index.rst +++ b/docs/source/api/models/lithium_ion/index.rst @@ -9,5 +9,6 @@ Lithium-ion Models mpm dfn newman_tobias + msmr yang2017 electrode_soh diff --git a/docs/source/api/models/lithium_ion/msmr.rst b/docs/source/api/models/lithium_ion/msmr.rst new file mode 100644 index 0000000000..89ac143e2e --- /dev/null +++ b/docs/source/api/models/lithium_ion/msmr.rst @@ -0,0 +1,7 @@ +Multi-Species Multi-Reaction (MSMR) Model +========================================= + +.. autoclass:: pybamm.lithium_ion.MSMR + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst index abf878e57b..522418a42f 100644 --- a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst +++ b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst @@ -1,5 +1,5 @@ -Butler Volumer -============== +Butler Volmer +============= .. autoclass:: pybamm.kinetics.SymmetricButlerVolmer :members: diff --git a/docs/source/api/models/submodels/interface/kinetics/index.rst b/docs/source/api/models/submodels/interface/kinetics/index.rst index 8def3d7fc8..efb8be4d30 100644 --- a/docs/source/api/models/submodels/interface/kinetics/index.rst +++ b/docs/source/api/models/submodels/interface/kinetics/index.rst @@ -10,5 +10,6 @@ Kinetics marcus no_reaction tafel + msmr_butler_volmer total_main_kinetics inverse_kinetics/index diff --git a/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst new file mode 100644 index 0000000000..18bea7ee7a --- /dev/null +++ b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst @@ -0,0 +1,5 @@ +MSMR Butler Volmer +================== + +.. autoclass:: pybamm.kinetics.MSMRButlerVolmer + :members: diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst index 132e5b88a9..fc664adf2b 100644 --- a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst @@ -6,3 +6,4 @@ Open-circuit potential models base_ocp current_sigmoid_ocp single_ocp + msmr_ocp diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst new file mode 100644 index 0000000000..f2106367d2 --- /dev/null +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst @@ -0,0 +1,8 @@ +MSMR Open Circuit Potential +=========================== + + +.. autoclass:: pybamm.open_circuit_potential.MSMROpenCircuitPotential + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/particle/index.rst b/docs/source/api/models/submodels/particle/index.rst index ae020ac3fa..b17a7502e4 100644 --- a/docs/source/api/models/submodels/particle/index.rst +++ b/docs/source/api/models/submodels/particle/index.rst @@ -8,3 +8,4 @@ Particle fickian_diffusion polynomial_profile x_averaged_polynomial_profile + msmr_diffusion diff --git a/docs/source/api/models/submodels/particle/msmr_diffusion.rst b/docs/source/api/models/submodels/particle/msmr_diffusion.rst new file mode 100644 index 0000000000..af7dfe2582 --- /dev/null +++ b/docs/source/api/models/submodels/particle/msmr_diffusion.rst @@ -0,0 +1,7 @@ +MSMR Diffusion +============== + +.. autoclass:: pybamm.particle.MSMRDiffusion + :members: + +.. footbibliography:: diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index 4287e28927..4bab430032 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -59,6 +59,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/lead-acid.ipynb notebooks/models/lithium-plating.ipynb notebooks/models/MPM.ipynb + notebooks/models/MSMR.ipynb notebooks/models/pouch-cell-model.ipynb notebooks/models/rate-capability.ipynb notebooks/models/SEI-on-cracks.ipynb diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb index 54101815af..ac34142fab 100644 --- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb @@ -199,6 +199,7 @@ "V_hat = pybamm.Parameter(\"Partial molar volume [m3.mol-1]\")\n", "c_inf = pybamm.Parameter(\"Bulk electrolyte solvent concentration [mol.m-3]\")\n", "\n", + "\n", "def D(cc):\n", " return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})" ] @@ -485,9 +486,11 @@ " {\"SEI layer\": {xi: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}\n", ")\n", "\n", + "\n", "def Diffusivity(cc):\n", " return cc * 10**(-12)\n", "\n", + "\n", "# parameter values (not physically based, for example only!)\n", "param = pybamm.ParameterValues(\n", " {\n", @@ -565,6 +568,7 @@ "L_0_eval = param.evaluate(L_0)\n", "xi = np.linspace(0, 1, 100) # dimensionless space\n", "\n", + "\n", "def plot(t):\n", " _, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", " ax1.plot(solution.t, L_out(solution.t) * 1e6)\n", @@ -581,6 +585,7 @@ " plt.tight_layout()\n", " plt.show()\n", " \n", + "\n", "import ipywidgets as widgets\n", "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1],step=0.1,value=0));" ] diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb index a4ecfdb427..25b79ec260 100644 --- a/docs/source/examples/notebooks/models/DFN.ipynb +++ b/docs/source/examples/notebooks/models/DFN.ipynb @@ -62,22 +62,22 @@ "\n", "#### Current:\n", "$$\n", - "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=1}=0, \\\\\n", + "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=L}=0, \\\\\n", "\\phi_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = \\phi_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\quad i_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = i_{\\text{e,s}}\\big\\vert_{x=L_{\\text{n}}} = I, \\\\ \n", - "\\phi_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}, \\quad \n", - " i_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}} = I.\n", + "\\phi_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}, \\quad \n", + " i_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}} = I.\n", "$$\n", "\n", "#### Concentration in the electrolyte:\n", "$$\n", - "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0,\\\\ \n", + "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0,\\\\ \n", "c_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = c_{\\text{e,s}}|_{x=L_{\\text{n}}}, \\quad N_{\\text{e,n}}\\big|_{x=L_{\\text{n}}}=N_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\\\\n", - "c_{\\text{e,s}}|_{x=1-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=1-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}.\n", + "c_{\\text{e,s}}|_{x=L-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=L-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}.\n", "$$\n", "\n", "#### Concentration in the electrode active material:\n", "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n", "$$\n", "\n", "#### Reference potential:\n", @@ -87,8 +87,8 @@ "#### And the initial conditions:\n", "\n", "$$\n", - "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\phi_{\\text{s,n}}(x,0) = 0, \\quad \\phi_{\\text{s,p}}(x,0) = \\phi_{\\text{s,p,0}}, \\\\ \\text{k} \\in \\text{n, p},\\\\\n", - "\\phi_{\\text{e,k}}(x,0) = \\phi_{\\text{e,0}}, \\quad c_{\\text{e,k}}(x,0) = 1, \\\\ \\text{k} \\in \\text{n, s, p}. \n", + "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\\\\\n", + "c_{\\text{e,k}}(x,0) = c_{\\text{e,0}}, \\quad \\text{k} \\in \\text{n, s, p}. \n", "$$\n" ] }, @@ -269,7 +269,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -287,7 +287,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb new file mode 100644 index 0000000000..7413339f5b --- /dev/null +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Species Multi-Reaction model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Equations\n", + "\n", + "Here we briefly outline the models used for the open-circuit potential, kinetics, and solid phase transport used in the MSMR model, as described in Baker and Verbrugge (2018). The remaining physics is modelled differently depending on which options are selected. By default, the rest of the battery model is as described in Maquis et al. (2019). In the following we give equations for a single electrode." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermodynamics\n", + "The MSMR model is developed by assuming that all electrochemical reactions at the electrode/electrolyte interface in a lithium insertion cell can be expressed in the form \n", + "$$ \\text{Li}^{+} + \\text{e}^{-} + \\text{H}_{j} \\rightleftharpoons (\\text{Li--H})_{j}.$$\n", + "For each species $j$, a vacant host site $\\text{H}_{j}$ can accommodate one lithium leading to a filled host site $(\\text{Li--H})_{j}$. The OCV for this reaction is written as\n", + "$$ U_j = U_j^0 + \\frac{\\omega_j}{f}\\log\\left(\\frac{X_j - x_j}{x_j}\\right),$$\n", + "where $f = (RT)/F$, and $R$, $T$, and $F$ are the universal gas constant, temperature in Kelvin, and Faraday’s constant, respectively. Here $X_j$ represents the total fraction of available host sites which can be occupied by species $j$, $x_j$ is the fraction of filled sites occupied by species $j$, $U_j^0$ is a concentration independent standard electrode potential, and the $\\omega_j$ is an unitless parameter that describes the level of disorder of the reaction represented by gallery $j$. \n", + "\n", + "The equation for each reaction can be inverted to give \n", + "$$x_j = \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", + "The overall electrode state of charge is given by summing the fractional occupancies \n", + "$$x = \\sum_j x_j = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]},$$\n", + "which is an explicit closed form expression for the inverse of the OCV. This is opposite to many battery models where one typically gives the OCV as an explicit function of the state of charge (or stoichiometry).\n", + "\n", + "At a particle interface with the electrolyte, local equilibrium requires that \n", + "$$U_j = U(x) \\quad \\forall j.$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kinetics\n", + "The kinetics of the insertion reaction are given as\n", + "$$i_j = i_{0,j}[e^{(1-\\alpha_j)f\\eta} - e^{-\\alpha_jf\\eta}], \\qquad i = \\sum_j i_j,$$\n", + "where $i_j$ is the interfacial current associated with reaction $j$, $\\alpha_j$ is the symmetry factor, $\\eta$ is the overpotential, given by \n", + "$$ \\eta = \\phi_s - \\phi_e - U(x),$$\n", + "where $\\phi_s$ and $\\phi_e$ are the solid phase and electrolyte potentials, respectively, and $i_{0,j}$ is the exchange current density of reaction $j$, given by\n", + "$$i_{0,j} = i_{0,j}^{ref}(x_j)^{\\omega_j\\alpha_j}(X_j-x_j)^{\\omega_j(1-\\alpha_j)}(c_e/c_e^{ref})^{1-\\alpha_j},$$\n", + "where $c_e$ is the electrolyte concentration." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solid phase transport\n", + "Within the MSMR framework, the flux within the particles is expressed in terms of gradient of the chemical potential\n", + "$$N = -c_{\\text{T}}x\\frac{D}{RT}\\nabla \\mu + x(N+N_{\\text{H}}),$$\n", + "where $N$ is the flux of lithiated sites, $N_{\\text{H}}$ is the flux of unlithiated sites, $c_{\\text{T}}$ is the total concentration of lithiated and delithiated sites, and $D$ is a diffusion coefficient. Ignoring volumetric expansion during lithiation, the total flux of sites vanishes\n", + "$$N+N_{\\text{H}}.$$ \n", + "It can then be shown that \n", + "$$N = c_{\\text{T}}fDx(1-x)\\frac{\\text{d}U}{\\text{d}x}\\nabla x.$$\n", + "\n", + "A mass balance in the solid phase then gives\n", + "$$\\frac{\\partial x}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\frac{\\text{d}U}{\\text{d}x}\\nabla x\\right),$$\n", + "which, for a radially symmetric spherical particle, must be solved subject to the boundary conditions\n", + "$$N\\big\\vert_{r=0} = 0, \\quad N\\big\\vert_{r=R} = \\frac{i}{F},$$\n", + "where $R$ is the particle radius. This must be supplemented with a suitable initial condition for the electrode state of charge.\n", + "\n", + "Solution of this problem requires evaluate of the function $U(x)$ and the derivative $\\text{d}U/\\text{d}x$, but these functions cannot be explicitly integrated. This problem can be avoided by replacing the dependent variable $x$ with a new dependent variable $U$ subject to the transformation \n", + "$$x = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", + "This gives the following equation for mass balance within the particles\n", + "$$\\frac{\\text{d}U}{\\text{d}x}\\frac{\\partial U}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\nabla x\\right),$$\n", + "\n", + "which must be solved along with the transformed boundary and initial conditions." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameterization of the MSMR model\n", + "The behaviour of MSMR model is characterised by the parameters $X_j$, $U^0_j$, $\\omega_j$, $\\alpha_j$, and $i_{0,j}^{ref}$. Let's take a look at their values in the example parameter set provided in PyBaMM. The thermodynamic parameter values are taken from Verbrugge et al. (2017) and correspond to a graphite negative electrode and NMC positive electrode. The remaining value are based on a parameterization of the LG M50 cell, from Chen et al. (2020).\n", + "\n", + "We first load in the MSMR model and specify the number of reactions in each electrode" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can inspect the parameter values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "negative electrode:\n", + "X_n_0 = 0.43336, U0_n_0 = 0.08843, w_n_0 = 0.08611, a_n_0 = 0.5 j0_ref_n_0 = 2.7\n", + "X_n_1 = 0.23963, U0_n_1 = 0.12799, w_n_1 = 0.08009, a_n_1 = 0.5 j0_ref_n_1 = 2.7\n", + "X_n_2 = 0.15018, U0_n_2 = 0.14331, w_n_2 = 0.72469, a_n_2 = 0.5 j0_ref_n_2 = 2.7\n", + "X_n_3 = 0.05462, U0_n_3 = 0.16984, w_n_3 = 2.53277, a_n_3 = 0.5 j0_ref_n_3 = 2.7\n", + "X_n_4 = 0.06744, U0_n_4 = 0.21446, w_n_4 = 0.0947, a_n_4 = 0.5 j0_ref_n_4 = 2.7\n", + "X_n_5 = 0.05476, U0_n_5 = 0.36325, w_n_5 = 5.97354, a_n_5 = 0.5 j0_ref_n_5 = 2.7\n", + "positive electrode:\n", + "X_p_0 = 0.13442, U0_p_0 = 3.62274, w_p_0 = 0.9671, a_p_0 = 0.5 j0_ref_p_0 = 5\n", + "X_p_1 = 0.3246, U0_p_1 = 3.72645, w_p_1 = 1.39712, a_p_1 = 0.5 j0_ref_p_1 = 5\n", + "X_p_2 = 0.21118, U0_p_2 = 3.90575, w_p_2 = 3.505, a_p_2 = 0.5 j0_ref_p_2 = 5\n", + "X_p_3 = 0.3298, U0_p_3 = 4.22955, w_p_3 = 5.52757, a_p_3 = 1 j0_ref_p_3 = 1000000.0\n" + ] + } + ], + "source": [ + "parameter_values = model.default_parameter_values\n", + "\n", + "# Loop over domains\n", + "for domain in [\"negative\", \"positive\"]:\n", + " print(f\"{domain} electrode:\")\n", + " d = domain[0]\n", + " # Loop over reactions\n", + " N = int(parameter_values[\"Number of reactions in \" + domain + \" electrode\"])\n", + " for i in range(N):\n", + " print(\n", + " f\"X_{d}_{i} = {parameter_values[f'X_{d}_{i}']}, \"\n", + " f\"U0_{d}_{i} = {parameter_values[f'U0_{d}_{i}']}, \"\n", + " f\"w_{d}_{i} = {parameter_values[f'w_{d}_{i}']}, \"\n", + " f\"a_{d}_{i} = {parameter_values[f'a_{d}_{i}']} \"\n", + " f\"j0_ref_{d}_{i} = {parameter_values[f'j0_ref_{d}_{i}']}\"\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the functional form of the open-circuit potential $U$, fractional occupancies $x_j$, and exchange current densities $i_{0,j}$ as a function of stoichiometry $x$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get symbolic parameters\n", + "param = model.param\n", + "param_n = param.n.prim\n", + "param_p = param.p.prim\n", + "\n", + "# set up ranges for plotting\n", + "U_n = pybamm.linspace(0.05, 1.1, 1000)\n", + "U_p = pybamm.linspace(2.8, 4.4, 1000)\n", + "\n", + "# get reference electrolyte concentration and temperature\n", + "c_e = param.c_e_init\n", + "T = param.T_init\n", + "\n", + "# set up figure\n", + "fig, ax = plt.subplots(3, 2, figsize=(10, 10))\n", + "colors = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n", + "\n", + "# sto vs potential\n", + "x_n = param_n.x(U_n)\n", + "x_p = param_p.x(U_p)\n", + "ax[0, 0].plot(parameter_values.evaluate(x_n), parameter_values.evaluate(U_n), \"k-\")\n", + "ax[0, 1].plot(parameter_values.evaluate(x_p), parameter_values.evaluate(U_p), \"k-\")\n", + "ax[0, 0].set_xlabel(\"x_n\")\n", + "ax[0, 0].set_ylabel(\"U_n [V]\")\n", + "ax[0, 1].set_xlabel(\"x_p\")\n", + "ax[0, 1].set_ylabel(\"U_p [V]\")\n", + "\n", + "# fractional occupancy vs potential\n", + "for i in range(6):\n", + " xj = param_n.x_j(U_n, i)\n", + " ax[1, 0].plot(\n", + " parameter_values.evaluate(x_n),\n", + " parameter_values.evaluate(xj),\n", + " color=colors[i],\n", + " label=f\"x_n_{i}\",\n", + " )\n", + "ax[1, 0].set_xlabel(\"x_n\")\n", + "ax[1, 0].set_ylabel(\"x_n_j\")\n", + "ax[1, 0].legend()\n", + "for i in range(4):\n", + " xj = param_p.x_j(U_p, i)\n", + " ax[1, 1].plot(\n", + " parameter_values.evaluate(x_p),\n", + " parameter_values.evaluate(xj),\n", + " color=colors[i],\n", + " label=f\"x_p_{i}\",\n", + " )\n", + "ax[1, 1].set_xlabel(\"x_p\")\n", + "ax[1, 1].set_ylabel(\"x_p_j\")\n", + "ax[1, 1].legend()\n", + "\n", + "# exchange current density vs potential\n", + "for i in range(6):\n", + " xj = param_n.x_j(U_n, i)\n", + " j0 = param_n.j0_j(c_e, U_n, T, i)\n", + " ax[2, 0].plot(\n", + " parameter_values.evaluate(x_n),\n", + " parameter_values.evaluate(j0),\n", + " color=colors[i],\n", + " label=f\"j0_n_{i}\",\n", + " )\n", + "ax[2, 0].set_xlabel(\"x_n\")\n", + "ax[2, 0].set_ylabel(\"j0_n_j [A.m-2]\")\n", + "ax[2, 0].legend()\n", + "for i in range(4):\n", + " xj = param_p.x_j(U_p, i)\n", + " j0 = param_p.j0_j(c_e, U_p, T, i)\n", + " ax[2, 1].plot(\n", + " parameter_values.evaluate(x_p),\n", + " parameter_values.evaluate(j0),\n", + " color=colors[i],\n", + " label=f\"j0_p_{i}\",\n", + " )\n", + "ax[2, 1].set_ylim([0, 0.5])\n", + "ax[2, 1].set_xlabel(\"x_p\")\n", + "ax[2, 1].set_ylabel(\"j0_p_j [A.m-2]\")\n", + "ax[2, 1].legend()\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example solving MSMR using PyBaMM\n", + "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We already created the model in the previous section, so we can go ahead and define our experiment, before creating and solving a simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 275.026 and h = 2.68649e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.028 and h = 4.19765e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 1C for 1 hour or until 3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at C/3 until 4.2 V\",\n", + " \"Hold at 4.2 V until 10 mA\",\n", + " \"Rest for 1 hour\",\n", + " ),\n", + " ],\n", + " period=\"10 seconds\",\n", + ")\n", + "sim = pybamm.Simulation(model, experiment=experiment)\n", + "sim.solve()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can plot the results. In the MSMR model we can look at both the potential and stoichiometry as a function of position through the electrode and within the particle" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "67da37f9dcb64ac696aca8772d5ffce7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.plot(\n", + " [\n", + " \"Negative particle stoichiometry\",\n", + " \"Positive particle stoichiometry\",\n", + " \"X-averaged negative electrode open-circuit potential [V]\",\n", + " \"X-averaged positive electrode open-circuit potential [V]\", \n", + " \"Negative particle potential [V]\",\n", + " \"Positive particle potential [V]\",\n", + " \"Current [A]\",\n", + " \"Voltage [V]\",\n", + " ],\n", + " variable_limits=\"tight\", # make axes tight to plot at each timestep\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at the individual reactions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b056c49819644d6848340ae609978f2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xns = [f\"Average x_n_{i}\" for i in range(6)] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n", + "xps = [f\"Average x_p_{i}\" for i in range(4)] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n", + "sim.plot(\n", + " [\n", + " xns,\n", + " xps,\n", + " \"Current [A]\",\n", + " \"Negative electrode stoichiometry\",\n", + " \"Positive electrode stoichiometry\",\n", + " \"Voltage [V]\",\n", + " ]\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and plot how they sum to give the electrode stoichiometry " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sim.solution\n", + "time = sol[\"Time [h]\"].data\n", + "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", + "\n", + "ax[0].plot(time, sol[\"Average negative particle stoichiometry\"].data, \"k-\", label=\"x_n\")\n", + "bottom = 0\n", + "for xn in xns:\n", + " top = bottom + sol[xn].data\n", + " ax[0].fill_between(time, bottom, top, label=xn[-4:])\n", + " bottom = top\n", + "ax[0].set_xlabel(\"Time [h]\")\n", + "ax[0].set_ylabel(\"x_n [-]\")\n", + "ax[0].legend(\n", + " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", + ")\n", + "ax[1].plot(time, sol[\"Average positive particle stoichiometry\"].data, \"k-\", label=\"x_p\")\n", + "bottom = 0\n", + "for xp in xps:\n", + " top = bottom + sol[xp].data\n", + " ax[1].fill_between(time, bottom, top, label=xp[-4:])\n", + " bottom = top\n", + "ax[1].set_xlabel(\"Time [h]\")\n", + "ax[1].set_ylabel(\"x_p [-]\")\n", + "ax[1].legend(\n", + " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[5] Charles R. Harris, K. Jarrod Millman, StΓ©fan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[8] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", + "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[10] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "vscode": { + "interpreter": { + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb index 978951220b..1548caa623 100644 --- a/docs/source/examples/notebooks/models/SPMe.ipynb +++ b/docs/source/examples/notebooks/models/SPMe.ipynb @@ -25,7 +25,7 @@ "\n", "ii) At the centre of each particle the standard no-flux condition is imposed, and the flux on the surface of the particle is simply the current $I$ divided by the thickness of the electrode $L_{\\text{k}}$, as in the SPM. Since lithium is transferred between the electrolyte and particles, the flux through the particle surface also enters the electrolyte diffusion equation as a source/sink term. There is no transfer of lithium between the electrolyte and current collectors, which leads to no flux boundary conditions on the lithium concentration in the electrolyte $c_{\\text{e,k}}$ at either end of the cell. \n", "\n", - "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) = 0$. \n", + "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) = c_{\\text{e,0}}$. \n", "\n", "\n", "The model equations for the SPMe read: \n", @@ -33,20 +33,18 @@ "\n", "#### Particles: \n", "$$\n", - "\\mathcal{C}_{\\text{k}} \\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", + "\\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", "N_{\\text{s,k}} = -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p},\n", "$$\n", "\n", "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \n", "\\begin{cases}\n", - "\t\t \\frac{I}{L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", - "\t\t -\\frac{I}{L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", + "\t\t \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", + "\t\t -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", "\\end{cases} \\\\\n", - "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\n", "$$\n", - "\n", - "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n", + "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,R_{\\text{k}}]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n", "\n", "\n", "#### Electrolyte: \n", @@ -66,10 +64,10 @@ "$$\n", "\n", "$$\n", - "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0, \\\\\n", + "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0, \\\\\n", "c_{\\text{e,k}}(x,0) = 0, \\quad \\text{k} \\in \\text{n, s, p},\n", "$$\n", - "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,1]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n", + "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,L]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n", "\n", "### Voltage Expression\n", "The voltage is obtained from the expression: \n", @@ -90,7 +88,7 @@ "where\n", "$$\n", "\\bar{c}_{\\text{e,n}} = \\frac{1}{L_{\\text{n}}}\\int_0^{L_{\\text{n}}} c_{\\text{e,n}} \\, \\text{d}x, \\quad\n", - "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{1-L_{\\text{p}}}^{1} c_{\\text{e,p}} \\, \\text{d}x.\n", + "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{L-L_{\\text{p}}}^{L} c_{\\text{e,p}} \\, \\text{d}x.\n", "$$\n", "\n", "More details can be found in [[3]](#References)." @@ -248,7 +246,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -266,7 +264,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb index d3d5159a66..bc04f92fbc 100644 --- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb +++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -64,14 +64,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5 C\n", + "0.5 C\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "1 C\n", "2 C\n" ] @@ -125,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -155,12 +161,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -240,12 +246,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -296,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": {}, "outputs": [ { diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb index 6582e3e5b1..e23e1ee15f 100644 --- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb +++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb @@ -24,16 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-16T18:29:52.808793Z", + "start_time": "2023-09-16T18:29:52.652390Z" } - ], + }, + "outputs": [], "source": [ "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", "import pybamm\n", @@ -54,7 +51,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-16T18:29:52.814163Z", + "start_time": "2023-09-16T18:29:52.810Z" + } + }, "outputs": [], "source": [ "C_rates = {\"01\": 0.1, \"05\": 0.5, \"1\": 1, \"2\": 2, \"3\": 3}" @@ -70,7 +72,12 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-16T18:29:53.239553Z", + "start_time": "2023-09-16T18:29:53.042900Z" + } + }, "outputs": [], "source": [ "# load model and geometry\n", @@ -110,18 +117,29 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-16T18:29:54.214804Z", + "start_time": "2023-09-16T18:29:53.240926Z" + } + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/z4/5lmf5d5d23sc2gkhs__zfnfc0000gn/T/ipykernel_2839/4153409347.py:5: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n", + " discharge_curve = plt.subplot(211)\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -142,6 +160,7 @@ "plt.grid(True)\n", "plt.xlabel(r\"Discharge Capacity (Ah)\")\n", "plt.ylabel(r\"$\\vert V - V_{comsol} \\vert$\")\n", + "colors = iter(plt.cycler(color='bgrcmyk'))\n", "\n", "# loop over C_rates dict to create plot\n", "for key, C_rate in C_rates.items():\n", @@ -175,7 +194,7 @@ " voltage_difference = np.abs(voltage_sol[0:end_index] - comsol_voltage[0:end_index])\n", "\n", " # plot discharge curves and absolute voltage_difference\n", - " color = next(ax._get_lines.prop_cycler)[\"color\"]\n", + " color = next(colors)[\"color\"]\n", " discharge_curve.plot(\n", " comsol_discharge_capacity, comsol_voltage, color=color, linestyle=\":\"\n", " )\n", @@ -209,7 +228,12 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-16T18:29:54.229743Z", + "start_time": "2023-09-16T18:29:54.225157Z" + } + }, "outputs": [ { "name": "stdout", @@ -227,6 +251,13 @@ "source": [ "pybamm.print_citations()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -245,7 +276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.18" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb index 5d78554b9a..4d32f6a40e 100644 --- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb +++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb @@ -20,20 +20,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Invalid requirement: '#'\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -55,18 +49,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b55c57e62d4444fb157f8a1bbdde58c", + "model_id": "17f51c91ccd74aeb9afa13693702858e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=2.32485835391946, step=0.0232485835391946), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=2.3248351274860397, step=0.0232483512748604)…" ] }, "metadata": {}, @@ -75,10 +69,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -146,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -165,18 +159,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x_100 : 0.83337428922595\n", - "y_100 : 0.03354553395256055\n", - "Q : 4.968932758817601\n", - "x_0 : 0.0015118453536460735\n", - "y_0 : 0.8908948803914055\n" + "x_100 : 0.8333742766485323\n", + "y_100 : 0.03354554691532985\n", + "Q : 4.968932683689383\n", + "x_0 : 0.0015118453536460618\n", + "y_0 : 0.8908948803914054\n" ] } ], @@ -244,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -252,10 +246,10 @@ "output_type": "stream", "text": [ "x_100 : 0.833374276202919\n", - "y_100 : 0.03354554737459606\n", + "y_100 : 0.0335455473745959\n", "Q : 4.968932679279884\n", - "x_0 : 0.0015118456462390728\n", - "y_0 : 0.8908948800898482\n" + "x_0 : 0.0015118456462390713\n", + "y_0 : 0.890894880089848\n" ] } ], @@ -288,12 +282,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -350,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -406,12 +400,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -466,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -487,7 +481,17 @@ }, { "data": { - "image/png": "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", + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", "text/plain": [ "
" ] @@ -497,7 +501,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -507,7 +511,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -517,7 +521,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -527,7 +531,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -537,7 +541,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABRQAAAEiCAYAAACIil5LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADMQklEQVR4nOzdd3hURRfA4d+mJ6RISKhSBRGkVwEREJCigChFQAxdigrSUSmCgIIgKIiAfjQRpIOAoCIgojSVJr1EeoeE9GR3vj8m2WRTlgSS3JTzPs8+udk9e/fkbjK5O3dmjkkppRBCCCGEEEIIIYQQQohUcDA6ASGEEEIIIYQQQgghRPYhHYpCCCGEEEIIIYQQQohUkw5FIYQQQgghhBBCCCFEqkmHohBCCCGEEEIIIYQQItWkQ1EIIYQQQgghhBBCCJFq0qEohBBCCCGEEEIIIYRINelQFEIIIYQQQgghhBBCpJp0KAohhBBCCCGEEEIIIVJNOhSFEEIIIYQQQgghhBCpJh2KQgghhBBCCCGEEEKIVJMORSGEECIT/Pvvv7z++usUKVIEV1dXChcuTJcuXfj3339t4hYuXIjJZOLAgQM29wcFBVGrVi3c3NzYsmVLZqaeRFhYGLNnz+aFF16gUKFCeHl5UbVqVebMmYPZbE4Sb7FYmDJlCiVLlsTNzY1KlSqxbNmyJDELFy6kdevWFC1alDx58lChQgU++ugjIiIibGIvXrzIhx9+SK1atcibNy9+fn40bNiQX375JUN/biGEEEIIIYQmHYpCCCFEBluzZg3VqlVj27ZtdO/enS+//JKePXuyfft2qlWrxtq1a+0+Pzg4mBdeeIHDhw+zdu1amjdvnkmZJ+/cuXO8/fbbKKUYPHgwn376KSVLlqR///706NEjSfz777/PiBEjaNq0KV988QXFihWjc+fOLF++3BoTFhZG9+7duXnzJn379mXGjBnUqlWLsWPH0qJFC5RS1tj169fzySefULp0aT766CNGjx7N/fv3adq0KQsWLMiUYyCEEEIIIURuZlIJz9CFEEIIka7Onj1LpUqVKFasGL/99hv+/v7Wx27dukX9+vW5ePEihw8fplSpUixcuJDu3buzf/9+atSowf3793nhhRf4+++/WbNmDS+++KKBP0183tevX+fpp5+2ub9Hjx4sWLCA06dPU7p0aQAuX75MyZIl6dOnD7NmzQJAKUWDBg04f/48gYGBODo6EhUVxYEDB6hbt67NPsePH8/YsWP5+eefadKkCaBHexYoUAA/Pz9rXGRkJFWqVCEkJISLFy9m5I8vhBBCCCFEricjFIUQQogMNHXqVMLCwpg3b55NZyKAn58fc+fOJTQ0lClTpiR5bkhICM2bN+fvv/9m9erVSToT169fz4svvkjhwoVxdXXliSeeYMKECUmmHTds2JAKFSpw7NgxGjVqhIeHB0WKFEn2NSMjIxk7diylS5fG1dWVokWLMnz4cCIjI23yTtyZCNC2bVsAjh8/bpNjdHQ0/fv3t95nMpno168fly5d4s8//wTAxcUlSWdiSvt8+umnbToTAVxdXWnZsiWXLl3i/v37SfYjhBBCCCGESD9ORicghBBC5GQ//PADJUqUoH79+sk+/txzz1GiRAk2bdpkc39oaCgtWrRg//79rFq1ipdeeinJcxcuXIinpyeDBw/G09OTX3/9lTFjxhAcHMzUqVNtYu/evUvz5s155ZVX6NChA6tWrWLEiBFUrFiRFi1aAHodw9atW/P777/Tp08fypUrx5EjR/jss884deoU69ats/uzXrt2DcCms++ff/4hT548lCtXzia2Vq1a1sefffbZNO3TXqyHhwceHh4PjBVCCCGEEEI8POlQFEIIITJIUFAQV65coU2bNnbjKlWqxIYNG2xG1gUEBHDlyhVWrlxJ69atk33ed999h7u7u/X7vn370rdvX7788ks++ugjXF1drY9duXKFxYsX07VrVwB69uxJ8eLF+eabb6wdit999x2//PILO3futOnkq1ChAn379uWPP/5IdhQhQFRUFDNmzKBkyZLUrFnTev/Vq1cpUKAAJpPJJr5QoULWvOyZMmUK3t7e1hxTcubMGdasWUP79u1xdHS0GyuEEEIIIYR4NDLlWQghhMggcR2EXl5eduPiHg8ODrbed/36ddzc3ChatGiKz0vYmXj//n3rmoxhYWGcOHHCJtbT05PXX3/d+r2Liwu1atXi3Llz1vtWrlxJuXLleOqpp7h165b19vzzzwOwffv2FHN56623OHbsGLNmzcLJKf56ZXh4uE3HZhw3Nzfr4ymZNGkSv/zyCx9//DGPPfZYinFhYWG0b98ed3d3Pv744xTjhBBCCCGEEOlDOhSFEEKIDBLXUfigNf2S63icO3cuLi4uNG/enJMnTyb7vH///Ze2bdvi4+ODt7c3/v7+1k7DoKAgm9jHH388ySjBvHnzcvfuXev3p0+f5t9//8Xf39/m9uSTTwJw48aNZPOYOnUq8+fPZ8KECbRs2dLmMXd3d5v1F+NERERYH0/O999/zwcffEDPnj3p169fsjEAZrOZ1157jWPHjrFq1SoKFy6cYqwQQgghhBAifciUZyGEECKD+Pj4UKhQIQ4fPmw37vDhwxQpUgRvb2/rfeXLl2fz5s00btyYpk2bsnv3bpvRivfu3aNBgwZ4e3szfvx4nnjiCdzc3Pj7778ZMWIEFovF5jVSmgaslLJuWywWKlasyPTp05ONTW605MKFCxkxYgR9+/blgw8+SPJ4oUKF2L59O0opmw7Nq1evAiTbAfjzzz/zxhtv8OKLL/LVV18lm0uc3r17s3HjRpYuXWodSSmEEEIIIYTIWNKhKIQQQmSgl156ifnz5/P7778nW3xk165dBAYG8uabbyZ5rFatWqxbt44XX3yRpk2bsmvXLmul6B07dnD79m3WrFnDc889Z33O+fPnHzrXJ554gkOHDtG4ceMkoxmTs379enr16sUrr7zC7Nmzk42pUqUKX3/9NcePH6d8+fLW+/fu3Wt9PKG9e/fStm1batSowYoVK2ymTyc2bNgwFixYwIwZM+jUqVMqfkIhhBBCCCFEepApz0IIIUQGGjZsGO7u7rz55pvcvn3b5rE7d+7Qt29fPDw8GDZsWLLPb9y4McuWLePMmTM0b97cus5i3IjDhCMMo6Ki+PLLLx861w4dOnD58mXmz5+f5LHw8HBCQ0Ot3//222+89tprPPfccyxduhQHh+RPKdq0aYOzs7NNXkopvvrqK4oUKWJT5OX48eO8+OKLlChRgo0bN6Y4HRr0NOtPP/2U9957j4EDBz7MjyuEEEIIIYR4SDJCUQghhMhAZcqUYdGiRXTp0oWKFSvSs2dPSpYsSWBgIN988w23bt1i2bJlPPHEEynuo23btsyfP58ePXrQunVrtmzZQt26dcmbNy8BAQG88847mEwmlixZYtPBmFZdu3ZlxYoV9O3bl+3bt1OvXj3MZjMnTpxgxYoVbN26lRo1avDff//RunVrTCYT7dq1Y+XKlTb7qVSpEpUqVQL02o2DBg1i6tSpREdHU7NmTdatW8euXbtYunSptWP0/v37NGvWjLt37zJs2DA2bdpks88nnniCOnXqALB27VqGDx9OmTJlKFeuHN9++61NbNOmTSlQoMBDHwchhBBCCCGEfdKhKIQQQmSw9u3b89RTTzF58mRrJ2K+fPlo1KgR7733HhUqVHjgPrp3786dO3cYOnQo7du3Z+3atWzcuJEhQ4bwwQcfkDdvXl5//XUaN25Ms2bNHipPBwcH1q1bx2effcbixYtZu3YtHh4elCpVioEDB1qLs5w/f95a9GXAgAFJ9jN27FhrhyLAxx9/TN68eZk7dy4LFy6kTJkyfPvtt3Tu3Nkac/v2bS5evAjAyJEjk+wzICDA2qF46NAhQBeR6dq1a5LY7du3S4eiEEIIIYQQGcikHmUogxBCCCGEEEIIIYQQIleRNRSFEEIIIYQQQgghhBCpJh2KQgghhBBCCCGEEEKIVJMORSGEEEIIIYQQQgghRKpJh6IQQgghhBBCCCGEECLVpENRCCGEEEIIIYQQQgiRatKhKIQQQgghhBBCCCGESDUnoxPIbBaLhStXruDl5YXJZDI6HSFEFqCU4v79+xQuXBgHh9x9nUXaSCFEYtJGatI+CiESk/ZRCJGb5boOxStXrlC0aFGj0xBCZEEXL17k8ccfNzoNQ0kbKYRISW5vI6V9FEKkJLe3j0KI3CnXdSh6eXkButH39vY2OBuRJYWGQuHCABQEwtEfIvLkyWNoWiLjBAcHU7RoUWv7kJtJGynskvYxV5I2UpP2UdiVoH0MPXOGwqVLA9JG5nTSPgohcrNc16EYN0XF29tbTgZF8hwdrZtxE5q8vb3lZDAXkCls0kaKB5D2MVfL7W2ktI/CrgTto2OCziVpI3OH3N4+CiFyJ1noQQghhBBCCCGEEEIIkWrSoSiEEEIIIYQQQgghhEg1QzsUf/vtN1q1akXhwoUxmUysW7fugc/ZsWMH1apVw9XVldKlS7Nw4cIMz1MIITKbtI9CCCGEEEIIIbIqQ9dQDA0NpXLlyvTo0YNXXnnlgfHnz5/nxRdfpG/fvixdupRt27bRq1cvChUqRLNmzTIhY5EruLnB9u2YzWY2ATg64ubmZnRWIpeR9lFkSdI+CiFE8mLbRwC3xx5je9y2tJFCCCFyKJNSShmdBOiFbNeuXcvLL7+cYsyIESPYtGkTR48etd732muvce/ePbZs2ZKq1wkODsbHx4egoCBZUFsIAWT9diGz2kfI+sdCCJH5pF3Q5DgIIRKTdkEIkZtlqyrPf/75J02aNLG5r1mzZgwaNMiYhIRIxvHj1gvUNrJG171ITni40Rk8OmkfRU5nNkNERNJbZGTy98fdpO19dDmhjRQiq1MKfvwRLl8GDw/Ik0d/jbsl/t7NDaSwsBBCCCNlqw7Fa9euUaBAAZv7ChQoQHBwMOHh4bi7uyd5TmRkJJGRkdbvg4ODMzxPkc1FR8O8eZjNZuYBFkdH+vTpg7Oz8wOfunw5dOumP+AKkZkepn0EaSNFGj1C+5hW8+fDuHEQFKTb1JiYdH8JIYRIP7HtI0B09+7MW7AAIFVtZGQk9O4NS5ak/uUcHWHoUPj444fOWAghhHgk2apD8WFMnjyZDz/80Og0RHYSFQVvvYUjMBQIA7p162b3ZFApmDQJPvhAf1+7NhQtmvqXlCvMxoqOhlTUPMmRpI0UafIQ7ePD+P576NMn5cednPTonMQ3V9ek3zs6pmtquVJubiOFSLXY9hEgqn173ordflAbeesWtG0Lv/+u26tmzfSuQkMhLEzfEm5HRennmc0wZQp07AhVq2b4TyeEEEIkka06FAsWLMj169dt7rt+/Tre3t4pjr4ZNWoUgwcPtn4fHBxM0bT09AjxAFFR8OabEFdQd/BgfYInH2Kzj+Bg8PExOotH8zDtI0gbKbKe336DN97Q2/36wZAhSTsJnbLV2Uv2lxPaSCGyohMn4MUX4dw5/Te2ciU0bWr/OTExehmCPn30zJhBg2DHDrk4LYQQIvNlq1PyOnXqsHnzZpv7fv75Z+rUqZPic1xdXXF1dc3o1EQudfcuvPqqXjPRwQG++AL69zc6K5EbPUz7CNJGiqzl2DFo00ZfqHn5Zd2mysUZIURO9Msv0K6dXtahZEnYuBHKl3/w85ycwMtLX7xev15fhFmzRp+PCiGEEJnJwcgXDwkJ4eDBgxw8eBCA8+fPc/DgQS5cuADokTNvxA1TAPr27cu5c+cYPnw4J06c4Msvv2TFihW8++67RqQvcrkbN6BuXd2Z6OkJP/wgnYki/Uj7KHKbK1egRQu4dw/q1IHvvpPORCFEzrRiBTRvrjsT69WDvXtT15mYUNGiMGyY3h46VBehEkIIITKToR2KBw4coGrVqlSNXfhj8ODBVK1alTFjxgBw9epV64dngJIlS7Jp0yZ+/vlnKleuzLRp0/j6669p1qyZIfmL3G3wYD1VpUgRve5Ny5ZGZyRyEmkfRW5y/76e9nfhApQpAxs2gJ2Z+iKTbd9udAZC5Bw3b+qlcsxm6NJFj1T093+4fQ0frs9DAwNhxoz0zFIIIYR4MJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HZEWhoXrIIZAHXXQgJCSEPHnyWEN27IBGjfR6Nfv3Q/XqhmQq0om0C/HkWAi7UtE+plV0NLz0Evz0E+TPD3/+CaVKpU+64tGtWAEdOwYD0i5I+yjsStA+hl6/jmeBAkDSNrJXL/jmG6hSBQ4cePSR2N9+C1276pc+dQoKFXq0/Ym0kXZBCJGbGTpCUYjsKDoaBgzQ2/36SWeiEEI8LKV0YYGffgIPD72GmHQmZh1//BFfIEcI8ej27tWdiQCzZ6fPsg6dO0Pt2hASAu+//+j7E0IIIVIrWxVlESJTuLrCxo2YzWZWAsrR0aZoxeef68IBfn7w0UfGpSnST0iI0RkIkU08oH1Mq7FjYeFC/aF6xQqoWTPdMhWP6MwZXSAnMlKvbfnjj0ZnJEQWF9s+Arh6e7Mxbju2jTSb4y9IBwTodbjTg4ODnu5cp45uTwcMkIvdQgghMod0KAqRmJMTvPgijkDiZREvX4Zx4/T2lCmQN28m5yYyxKefGp2BENmEnfYxrebPhwkT9PacOXoNRZE13L6t1wW+dQtq1NAjqgoXNjorIbK42PYR9AesFxM1at98A3/9BT4+8Mkn6fvSzzyj12NcuhQGDdKVn02m9H0NIYQQIjGZ8ixEGgwdqkez1amjry6L7O/sWZg1y+gshMhdNm3SS0YAjB4NvXsbm4+IFxkJbdvC6dNQrBj88AM8whKZQgh0J/2oUXp7/HiIXV4xXU2erItZ/f47rFqV/vsXQgghEpMRikIkFh0NS5cSYzazVCmUkxNdunRh1y5nli/XU0tmz9ZfRfY3bJh+y4UQqZBC++js7JzqXRw4AB066Ol/AQHw4YcZmK9IE6WgRw/YtQu8vWHzZihYEIKDjc5MiGwgtn0EiO7QgaUrVgDQpUsXPvjAmTt3oGJF6N8/Y16+aFEYMULPpBk2TBe7cnfPmNcSQgghQKo8G52OyIqSqWJ6504I9erl4fhxeOst+OILQzMU6eTXX6FxY3BwCMZikXYBpI0UD/CIVZ7PndMjvG/cgBde0MuNpaEvUmSw0aP12sBOTnrNxCZN9P3SLmhyHIRdKVR53rUrhOeey4NSsHMnPPdcxqUQFgZly8KlSzBxIrz3Xsa9ltCkXRBC5GYyxkqIVJg9G44fB3//+DW/RPYWE6PXGQLo1cvQVITIFW7dgubNdWdilSp6Sp50JmYdCxbEFxqbOze+M1EI8WgGD9ajf7t0ydjORAAPj/j1GSdNgitXMvb1hBBC5G7SoShEKkyapL9OmQKPPWZoKiKdzJ8PR46Ar2/8ukZCiIwRFgatWsWvy7d5M3h5GZ2ViLNtG/Tpo7ffe09PexZCpI/9+/XAxalTM+f1OnXSRVpCQ2WEohBCiIwlHYpCpEJ4uL6qLIVYcoa7d/XUPtDrt/n6GpuPEDmZ2axH5uzZoy/IbNkChQoZnZWIc+wYvPqqHrX92msyCl+IjDBhQua1eyYTzJihtxct0uvWCiGEEBkh1xZlCQ0NxdHRMcn9jo6OuLm52cSlxMHBAfcEqx2nJTYsLIyUlq80mUx4eHg8VGx4eDgWiyXFPBKuc5WW2IiICMxmc7rEenh4YDKZAIiMjCQmJiZdYt3d3XGIrZQSFRVFtJ1KG3ZjQ0NJvBqYoyPMmQPR0fb36+bmZv29io6OJioqKsVYV1dXnJyc0hwbExNDZGRkirEuLi7WAglpiTWbzURERKQY6+zsjIuLS5pjLRYL4eHh6RLr5OSEq6srAEopwsLCHir2gw90xcVy5aBrV+z+LEKIh6cUDBwI69aBqyts2KD/7kTWcO0atGwJQUFQr56e9iwFxx5MziHlHDLZ2ATnjwnfzwoV9PrbD9pvep5DVqigLxAsXw4DB7ry++9OmExyDpke55CJyTmkECJXU7lMUFCQAlK8tWzZ0ibew8MjxdgGDRrYxPr5+aUYW6NGDZvY4sWLpxhbvnx5m9jy5cunGFu8eHGb2Bo1aqQY6+fnZxPboEGDFGM9PDxsYlu2bGn3uCXUrl07u7EhISHW2ICAALuxN27csMb279/fbuz58+etsUOHDrUbe/ToUWvs2LFjbX92/RlYqdhtQA0ZonOeMmWK3f1u377dut9Zs2bZjd24caM1dsGCBXZjV6xYYY1dsWKF3dgFCxZYYzdu3Gg3dtasWdbY7du3242dMmWKNXbfvn12Y8eOHWuNPXr0qN3YoUOHWmPPnz9vN7Z///7W2Bs3btiNDQgIsMaGhITYjW3Tpo0CVFBQkMrt4tpIORYiWSEhSdrHhG16YlOm6HCTSamVKzMxT/FAoaFK1ayp35/SpZW6eTPl2JzSLiT+fw+osmXLpvr5cg4ZT84htYS/U8mdPwJq2zZjzyFhhVq+XMfKOaQm55BCCJE+cu0IRSHSYsQIozMQQojsZdkyGD5cb0+fDu3aGZuPiBc3DX3/fsiXT69p6edndFaZ4+mnn+aXX36xfh83ykuIjFK7ttEZ6La4dWujsxBCCJHTmJRKYR5EDhUcHIyPjw9XzpzBO5kV4R1dXHBLUHUj9MaNFPfl4OSEe4LF19ISG3brFiqFqSImBwc8EpzZpyU2/M4dLHamdOTJn/+hYiPu3cNsZ+pFWmI9/PwwxU4ViQwOJsbOVIG0xLr7+uIQ+8EgKiSEaDvTE+zGxsRwdd5mPvzQzGrg7aHeTJzcHicnpwfu1+2xx3CMnXoRHRZGVEhIirGu3t44xU6NSktsTEQEkcHBKca6eHriHDuFKS2x5qgoIu7dSzHW2cMDF0/PNMdaYmIIv3MnXWKd3Nxw9fYGQFkshN26labYX36Bzl3AyRF+/x1KldKxoRERFChenKCgILxjn5NbxbWRcixEsmJiYO1azGYzawHl6Ejbtm2TdMps3w7NmkF0NLz7ru5QFFnH4MHw2Wfg4qILsjz7rP34nNIujBs3jnXr1nHw4MGHer71HPLKlWSPg0x5Tj4210x5jonBccMGNm+GDstb4u65mc8+c6Fbt1f1OWQmTnmOExYG1aq5cumSExMmwMiRMuUZSNcpz6GhoRQoUCDbt49CCPFQjBweaQTrtJ0E0xJsbommqygPj+TjQKlE01WUn1/KsYmmq6jixVOOTTRdRZUvn3JsoukqqkaNlGMTTVdRDRqkHJtouopq2TLl2MS/Ru3a2Y9NOD0uIMB+bILpKqp/f/uxCaarqKFD7ccmmK6ixo61H7tvX3xs3Py9lG4JpquoWbPsxyaYrqIWLLAfm2DKs1qxwn5sgukqauNG+7EJpquo7dvtxyaYrqL27bMfm2C6ijp61H5sgukq6vx5+7EJpquoGzfsxyaYrpJwmmZytyCZrmKVU6Y2CuMcPqyUj4/+82rXTimz2eiMREIJ/zUtW5a65+SUdmHs2LHKw8NDFSpUSJUsWVJ17txZ/ffff6l+vvU4XLmi/68kvoWH2z4huZi4W1jYw8eGhqYcGxr68LFhYfbzeNjY8PD0i7VY4mMjItIvNmFDFRn50LEXT4Qof48Q5UGIWvRliFIxManfb8LYqCj7sdHRqY5dtiTaemp/KTDa/n6jouL3G52G2JgY+7GRkQ8XazanX2xERHysxZJusUHXr+eI9lEIIR6GLL0thBBCiHRx6VJ8kY/69WHJEinykZVs3AjvvKO3J07URRtyk9q1a7Nw4UK2bNnCnDlzOH/+PPXr1+f+/fvJxkdGRhIcHGxzA6BwYfD0THp79VXbHeTPn3ycpye0aGEbW6JEyrHPPWcbW758yrE1a9rG1qyZcmz58raxzz2XcmyJEraxLVqkHJtg1gqgj0tKsbEzFKy6drUfm3Ck2Jtv2o9NOIth8GD7sRcuxMe+/7792OPH42MnTbJ57PGnPLkR5kkonrzR3xP+/js+duZM+/vdtSs+dt48+7Fbt8bHLl1qN7ajy1rq1NGHbs0ba+3vd+nS+P1u3Wo/dt68+Nhdu+zHzpwZH/v33/ZjJ02Kjz1+3H7s++/Hx164YD928OD42Fu37Me++WZ8bFiY/dg+fRBCiNwq9y4cc+UKJDcsPXHVPjvTmJN8SgoMTH3ssWN6gEByYqdnWO3fn/rY334DO1NQbPz4Y+pjV6/Wiy6lxpIlsHBhyo8nmF7D3Lkwe3bqYqdPhylTUo5NMB2IiRNh3LjUxb73HgwbBsDNm1CjSgz1gzbQ6TUL4c0U6tw52latqqeWDBwI/funvN8EU53o0we6dUs5NnYqBaAXs2rfPnWxbduCnenRxE79APScw9TG1q9vPzZ2WgsA1aqlPrZcudTHFitmPzbhtEo/v1TH3gz1oLJ3CEHB8OVsCAhIFBsaCuvXp7wvIYRmZ8pzUJDuTLx0Sf/Zr1tn2yQKY/39t+5AtFigZ08YNcrojDJfiwSdeJUqVaJ27doUL16cFStW0LNnzyTxkydP5sMPP8zMFEUOEQOsBfjll/hzSIOYTLo/r1Yt/THhbcMyEUIIkdPk2jUUZZ0LkZw+fWDp/FBC0VfM8wBhQEhIiM0aPyJ76dtX911Xrar75xNfN5B2IZ4cC2FXaKh1RFHC9tHZOQ8tWsCvv0LBgrBnDxQvbmimIoGLF3VhiKtXoUkTXYQl4bWcB8nJ7ULNmjVp0qQJkydPTvJYZGSkzRpywcHBFC1alKAU1lDE0dG2F93Ouog4ONhe3ExLbFiY/QvNCS/GpiU2PNz+heaE50FpiY2IsH9ROi2xHh7xF9MjI/VFjvSIdXePv/gfFaUXgE1DbFQU1K8Wyt7AAgCEnjuHZ+xCzdZzyAft180t/gQlOlrHp8TVNf7CaSpjAwJg6eIYnqsVybZtScckAPpCc1zjEBOjj1tKEsaazfq9S4mzc/xF7LTEWiz6dy09Yp2c4i/QK2U72vURYoNDQ/GRNRSFELlU7h2hKEQif/0FX38N7g8OFdnIoUMwf77enjkzaWeiEOLRWCzQo4fuTPT01J1V0pmYdQQHw4sv6s7EChVg1aq0dSbmZCEhIZw9e5auXbsm+7irq6u1MIONPHlsO8FSkpYLkWmJTdgJmJ6x7mk4A0pLbFqGKqcl1tXVdgZHesW6uNjO4EhF7Mwv4WhggvuTez/Tsl9n59T/oaYydtIkWLXKie37nFj+A3Tq9IAnODnZzgyxx9Ex9b/DaYl1cMiYWJMp/WJTO4NLCCFyIFnZSAj0xce339ZfO3YwOhuRXpSCQYN0h0eHDnpWtxAifY0bp5fdcnLSq2NUrWp0RiJOdLRu+44c0SNHN20CHx+jszLO0KFD2blzJ4GBgfzxxx+0bdsWR0dHOj2wZ0WIlF27BhMmGJ3FgxUpEr/UwfDh9gfoCSGEEKkhIxSFQH8Y/vNPfQHyo4+AFUZnJNLDmjWwY4ce8GBv+U2RVGhoKI7JDOd0dHTELcEIklA70/QcHBxwTzCKJS2xYWFhpLQih8lkwiPBqJu0xIaHh2OxM00v4dIGaYmNiIjAbGeUQlpiPTw8MMXORYuMjCTGzjS9tMS6u7vjEDtNLyoqimg7U+9SjA0NJfE4jenT9c8yfz40bBhFaGjK+3Vzc7P+XkVHRxNlZ5qeq6urdd2xtMTGxMTYTFNNzMXFBefY0TxpiTWbzUTYmabn7OyMS+zoo7TEWiwWwu1M00tLrJOTk3VEncWiePPNGLZudcbDQ7FyZQT58lmsM2sTxiqlCLPTu2DvZ8lOLl26RKdOnbh9+zb+/v48++yz7NmzB39/f6NTE9nYe+/B/ftQvxrw9wPDDTVkiG6rL1yATz+FMWOMzkgIIUS2ZmiNaQMEBQUpQAUFBRmdisgigoOVKlRIKVBq0iSlVEiI/gaUByhAhYSEGJ2mSKPwcKVKlNBv5ejR9mOlXYgXdyxSurVs2dIm3sPDI8XYBg0a2MT6+fmlGFujRg2b2OLFi6cYW758eZvY8uXLpxhbvHhxm9gaNWqkGOvn52cT26BBgxRjPTw8bGJbtmxp97gl1K5dO7uxCdubgIAAu7E3btywxvbv399u7Pnz562xQ4cOtRt79OhRa+zYsWPjf+7YtjFh+wg71YQJOnbKlCl297t9+3brfmfNmmU3duPGjdbYBQsW2I1dsWKFNXbFihV2YxcsWGCN3bhxo93YWbNmWWO3b99uN3bKlCnW2H379tmNHTt2rDX26NGjdmOHDh1qjT1//rzd2P79+1tjR4++H/tWmRW0ShIbEBBgjQ0JCbG73zZt2iiQNlL+V4jE9u2zNolq76/x548h168n26ZnBcuX6zQ9PJS6eNHobLI/aReEELmZTHkWud6kSXptqSeegHffNTobkV6mT9eF14sUgREjjM5GiJzrpZfg/feNzkIktHIlTJjgGfvdIOAHA7MRImdSCt55R2937aqrKGcHHTpAvXp6yvPIkUZnI4QQIjuTKs8iVzt9Wi9SHxUFGzZAq1akWMVUqjxnH1euwJNP6rdy6VLo3Nl+vLQL8eKOxZUzZ/D28kryuKOLC26PPWb9PvTGjRT35eDkhLuv70PFht26hUphurHJwQEPP7+Hig2/cweLnWnBefLnf6jYiHv3MNuZkpuWWA8/P0yx040jg4OJsTPdNC2x7r6+OMROC44KCSHazhTXFGNDQ8kTW7k0rn28feMOvv55U7Vft8cewzF2+m50WBhRISEpxrp6e+MUO70+LbExERFEBgenGOvi6Ylz7DT4tMSao6KIuHcvxVhnDw9cYv93pCXWEhND+J076RLr5ObG3/9606iRLs7ar1cYUycmf9yc3NxwjW3vlMVC2K1bKe43NCKCAsWL5/o2Uv5XiIS+/VZ3JObJA6dOQWGf+PPH0OvX8SygKz5nxXPIAwegZk29vWePrgIvHo60C0KIXM3oIZKZTYali4ReeklP+2jeXCmLJfbOqCilFixQ0V9/rRbOn68WLFigoqKiDM1TpM0bb+j3tU6dBO+rHdIuxLMeiwRTW21uiaY8Kw+P5ONAqURTnpWfX8qxiaY8q+LFU45NNOVZlS+fcmyiKc+qRo2UYxNNeVYNGqQcm2jKs2rZMuXYxP9q27WzH5twelxAgP3YBFOeVf/+9mMTTHlWQ4faj00w5VmNHZvk8WhQC0EtABX1xx/xsVOm2N9vginPatYs+7EJpjyrBQvsxyaY8qxWrLAfm2DKs9q40X5sginPavt2+7EJpjzbzINM7pZgyrM6etR+bIIpz+r8ebuxZ7qMsf6ZtW4eoWJwSDk+wZTnhEt9JHcLkinPSin5XyHi3b+vVOHC+k9k0qTYO2PPH9WCBSoqNFQtWLAgS59Dxp0rPfts6s6VRPKkXRBC5GZSlEXkWps3w8aNujLpjBkQW9cAnJ2hWzecgAAD8xMPZ98+WLxYb8+cmeB9FUKkG5v20UlOJbKCO+TlxU39uHUPqleH7+YE41gy5aJCQoiHN3myng1RqlSC5XJizx8BnIFusdtZ1cSJenmE33+HtWvhlVeMzkgIIUR2I1OeRa4UFQUVK+opKkOHwtSpRmck0oNSULeunr4TEAALF6buedIuxLMeiytXkj8Wjo66bHYcO5WbcXCABJWb0xQbFqbf0OSYTJCgcnOaYsPDwU7lZhJOS0tLbEQE2KncnKZYD4/4nvDISLAz7TpNse7u+jiDbgTtVHlOHBsTHk2HDrD1J8jnC7/+CqVLx8a6uenfi9TsN2FsdLSOT4mra3xnZVpiY2L0sUiJi4v+4J/WWLNZv3cpcXbW8WmNtVj079ojxEZGwgut3fhttyPFiuk2sFBBpf82UuLkpI8b6L8fO7HBoaH4FCiQaW2kb4LlD1LDZDLx999/U7x48QzKSJP/FQLg/HkoV07/3a1bB23aGJ3Rwxs9Gj76SLfn//4b39SI1JN2QQiRmxk+rGD27NlMnTqVa9euUblyZb744gtq2VnVeMaMGcyZM4cLFy7g5+dHu3btmDx5Mm4JP+AK8QCzZunOxPz59cmUjZgY2LoVs9nMVkA5OtKsWTOcZBROlvfdd/qDtKenHj2QExjWRubJY9sJZi8uLftMrYSdgOkZm7DTMj1j03J80xLr6hrf6ZOesS4uqf7kqJxd6P+WC2t/gjyuMfz03lZK/mdm838J2seH2C/OzvGddekZ6+SU+lGTaYl1dEz973BaYh0cHilWKejVF37bDd7esGkTFCoEYEr9fk0PiLXXAZ4B7t27x4wZM/Dx8XlgrFKK/v37Y87kHEXuNXy47kxs3Bhat07wQOz5I0BM48Zs3bYNIEufQw4fDvPnw5kzMGcODBxodEZCCCGyFSPnWy9fvly5uLio//3vf+rff/9VvXv3Vo899pi6fv16svFLly5Vrq6uaunSper8+fNq69atqlChQurdd99N9WvKOhfixg2lfHz0ujFff51MQIK1pDxAASok4ZpmIksKCVGqSJFE6xmlUlZtF6SNFFnBhAn678rBQakflkv7mNWMGaPfEicnpX76KWNeI7PbBZPJlGI7lxxPT0919uzZDMxIk/ZR/PZbfHt4+HCiBxOcP4Zcv67IJm3k3Lk6bV9fpe7cMTqb7EfaBSFEbuZgVEcmwPTp0+nduzfdu3enfPnyfPXVV3h4ePC///0v2fg//viDevXq0blzZ0qUKMELL7xAp06d2LdvXyZnLrKz0aMhKAiqVbMudSNygE8+gcuXoWTJBOsZZXPSRgqjLVwYP4p71ix46SVD0xGJLFoE48fr7a++gqZNjc0nvVgsFvInqIz+IPfv36dUbPVxITKKxQKDBunt3r310jk5QY8e8PTTcOeOXldRCCGESC3DOhSjoqL466+/aNKkSXwyDg40adKEP//8M9nn1K1bl7/++sv64fjcuXNs3ryZli1bZkrOIvs7dEhP7QBdiCVuKS+Rvf33X/w6mJ9+mrYZpVmVtJHCaD/9pD80A4wcCf36GZuPsLV9e/z789570LOnsfkIkdMtXgx//62XFojryM8JnJziz6G++ALOnTM2HyGEENmHYQt63Lp1C7PZTIECBWzuL1CgACdOnEj2OZ07d+bWrVs8++yzKKWIiYmhb9++vPfeeym+TmRkJJEJFlwPDg5Onx9AZDtK6SvLFgt06AD16xudkUgvw4fr+geNGkHbtkZnkz6kjRRG+ucfePVVvSRYly4yaiWrOX5ct3XR0fDaazBhgtEZZazTp0+zfft2bty4gSVRoaQxY8YYlJXITUJCYNQovT16tF6DOydp3lyPcP75Z/1zfv+90RkJIYTIDgyd8pxWO3bsYNKkSXz55Zf8/fffrFmzhk2bNjHBzpn05MmT8fHxsd6KFi2aiRmLrGTtWtixQ49emzLF6GxEevntN1ixQtcqmDEjvuBtbiRtpEgP//0HLVvqD9DPPw//+198wWdhvOvX9fsTFAT16sGCBTn7/Zk/fz7lypVjzJgxrFq1irVr11pv69atMzo9kUt88glcuwZPPAFvv210NunPZNIzPEwmfU6VwkQIIYQQwoZhIxT9/PxwdHTk+vXrNvdfv36dggULJvuc0aNH07VrV3r16gVAxYoVCQ0NpU+fPrz//vs4JHNGPWrUKAYPHmz9Pjg4WD4w50IRETB0qN4eNgyKFzc2H5E+zOb4ioR9+kClSsbmk56kjRRGuHNHj1S5dk2vD7ZmTeqLNouMFxamq8oGBkLp0rBuXc5Y4sGejz76iIkTJzJixAijUxG51H//6c420FODXV2NzSejVKoE3bvri0iDB8Mff+Tui7RCCCEezLBr2i4uLlSvXp1t27ZZ77NYLGzbto06deok+5ywsLAkH4gdYxfBU0ol+xxXV1e8vb1tbiL3+ewzOH8eihQB+UyScyxYAAcPgo9PzlrPCKSNFJkvIgLatIETJ+Dxx2HzZv23JbIGsxlefx327YN8+fT74+dndFYZ7+7du7Rv397oNEQuNnKkbh8bNoSXXzY6m4w1YQJ4eMCePbBqldHZCCGEyOoMG6EIMHjwYAICAqhRowa1atVixowZhIaG0r17dwDeeOMNihQpwuTJkwFo1aoV06dPp2rVqtSuXZszZ84wevRoWrVqZf3QLERiV6/CpEl6+5NPIE+eBzzBxQVmzcJsNvMpYHF0xEWG6GQ5QUG6EAHAuHHg729oOhlC2kiRWSwW3Vn1+++6E/HHH3WnYhLSPhpm+HC9dIeLix6ZWKaM0Rlljvbt2/PTTz/Rt29fo1MRudAff8Dy5Xqk3mefPWDEXmz7CODi6cmsuO1s1EYWLqzbmnHj9AX41q1z7ohMIYQQj87QDsWOHTty8+ZNxowZw7Vr16hSpQpbtmyxFiG4cOGCzWibDz74AJPJxAcffMDly5fx9/enVatWTJTV4oUdI0fqtcCeeQY6d07FE5ydYcAAHAEpapp1ffQR3LwJZcvCgAFGZ5MxpI0UmUEpPb1t9er4zqoKFVIIlvbREF9+CdOn6+1Fi+DZZ43NJ6N9/vnn1u3SpUszevRo9uzZQ8WKFXF2draJfeeddzI7PZFLWCzxy6r06AFVqjzgCbHtI4AzMCCbnpwMHQpz5+qZPbNmwZAhRmckhBAiqzKplObB5VDBwcH4+PgQFBQkU/tygb17dUci6GliNWsam49IH6dPw9NP6wqnmzdDixaPtj9pF+LJsch9pk+P/8C4bJmuGiyyjk2b9Cghi0VX27ZTtD3DZHa7ULJkyVTFmUwmzp07l8HZxJP2MXdZuFCvKejlpc87Yq/l5Qr/+x/07AmPPQZnzuhlFkTypF0QQuRmho5QFCIjWSzxlfi6d09DZ6LZDLt2YTab2QXg6Ej9+vVlymgWMmSI7kxs0eLROxOFyM2+/z6+M3Hq1FR0Jkr7mKn++Qc6dtT/z3r2hFGjjM4oc5w/f97oFEQud/9+/N/b6NGp7EyMbR8BzHXrsuuPPwCyZRsZEAAzZ8Lhw3pdxRkzjM5ICCFEViQjFEWOlfDK8qlTkEJh3KRCQ8HTE4A8QBgQEhJCngcuvigyw08/QbNm4OQER47AU089+j6lXYgnxyL32LkTXngBoqL0xZeZM1NR0VPax0xz6RLUrg1XrkCTJno0dqLZvplG2gVNjkPuMWoUfPyxrqZ+9Ggq1xFM0D6GXr+OZ2wvZHZtI3/+Wf+PcHKCY8dyz7qtaSXtghAiNzOsyrMQGSk4WK+dCPrKcqo7E0WWFh0N776rt996K306E4XIjf79V1crjYqCV15JRbEBkamCg+HFF3Vn4tNP62qrRnUmZlXr169n8eLFRqchcqCzZ+PXLJ0+PfcWJWnaVM8CiYnRBVqEEEKIxKRDUeRIH30E16/rq6lxC2qL7O+rr/RVcj8/GDPG6GyEyJ6uXNEfEu/dg3r14NtvIZvNxsvRoqOhQwc91bBgQb2Goo+P0VllPSNGjLBWvBciPQ0dqi+2NG0KL71kdDbGmjoVHBx0hfnY2dxCCCGElXQoihzn1Kn4tV4++0xXLRXZ3+3bMHas3p4wAfLmNTYfIbKj4GDdmXjxoq6Qvn49uLsbnZWIo5Qefb11K3h4wA8/QPHiRmeVNZ04cQKz2Wx0GiKH2bZNV7p3dJSR26BHSPfqpbeHDNHruQohhBBxpENR5DiDB8cX7HjxRaOzEell7Fi4excqVYLevY3ORojsJyoKXn1Vj3wrUAB+/FEqd2Y1U6fCvHm6E+O776BGDaMzyrru3bvHrFmzjE5D5CAxMTBokN7u3193pgkYP14vDbl/PyxfbnQ2QgghshLpUBQ5yo8/6ulhTk76yrLIGY4e1dOdQY8+lemZQqSNUnqUyS+/QJ48usBHyZJGZyUSWrkyfp2yzz6DNm2MzSer2rZtG507d6ZQoUKMjRu2/hA+/vhjTCYTg+J6kESuN2+ePt/w9YVx44zOJusoUCB+XfJRoyAiwth8hBBCZB3SoShyjMjI+PUSBw7U0/lE9qeULsRiNuviEY0aGZ2RENnPBx/AkiW6M37VKqhWzeiMREJ//gldu+rtt9+WtX8Tu3jxIuPHj6dkyZK88MILmEwm1q5dy7Vr1x5qf/v372fu3LlUqlQpnTMV2dWdO7qIH+gReb6+xuaT1bz7Ljz+OFy4ADNnGp2NEEKIrMLJ6ASESC+ffQanT+srqY9UsMPZGaZMISYmhvGAxckJZymvaZgfftCjqlxd9XRAIUTafPUVTJqkt+fPh+bNH2Fn0j6mu7NnoXVrfVGsVSsZXR8nOjqadevW8fXXX7Nr1y6aN2/O1KlT6dSpE++//z7ly5d/qP2GhITQpUsX5s+fz0cffZTOWYvsavRo3alYoQK8+eZD7iS2fQRw9vBgStx2DmgjPTxg4kQICND/T3r0AH9/o7MSQghhNJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HpJNLl/SIxLAwWLw4fqSHyN4iI/UaRmfP6mk2cZ0i6U3ahXhyLHKW9ev1yF6LBT78UKqjZzV37kDdunDypB41unOnXqssqzGiXcifPz9PPfUUr7/+Ou3btydvbCUuZ2dnDh069NAdigEBAfj6+vLZZ5/RsGFDqlSpwoy4Sm4PIO1jznTokP77s1hg+3Zo2NDojLImiwVq1oS//4YBA0CWMNWkXRBC5GYy5VnkCEOG6M7EZ5+F1183OhuRXj7/XHcmFiqkOxSFEKm3Zw906qQ/BPbqFT+dT2QNkZG6s/fkSShaFDZuzJqdiUaJiYnBZDJhMplwTKeFc5cvX87ff//N5MmTUxUfGRlJcHCwzU3kLErpZQYsFujQQToT7XFwgE8/1dtffQUnThibjxBCCONJh6LI9n79FVas0Cc6X3yhq2M+ErMZ9u/HvGcP+/fsYf/+/ZjN5nTJVaTe9eswYYLenjwZvLyMzUeI7OT0aT19NjwcWraEOXPSoW0EaR/TiVK6Wv3Onbpt27RJXzgR8a5cuUKfPn1YtmwZBQsW5NVXX2Xt2rWYHvIX+eLFiwwcOJClS5fi5uaWqudMnjwZHx8f661o0aIP9doi61q2DHbt0lN64zrLHlps+8j+/Zijoti/f3+OayMbNdL/W8zm+CJSQgghci+Z8iyytehoqFIFjh1Lx+kXoaHWYSJ5gDD0mkt58uRJh52L1OrVC775Rk+v2bNHdxhnFGkX4smxyP6uX9fTaM+dgxo19BS+dBv5Ju1juhg3Tk9Bd3LSFbebNjU6I/uMbhfOnj3LggULWLRoEZcvX6ZTp05069aN559/PtWjF9etW0fbtm1t4s1mMyaTCQcHByIjI5PsKzIyksjISOv3wcHBFC1aVNrHHCIkRC+Xc+UKfPQRvP/+I+4wQfsYev06ngUKxL5OzmojT5zQa02azTJFHIxvH4UQwkgyQlFka7Nm6c5EPz9dlU/kDH/9Bf/7n96eOTNjOxOFyElCQ+Gll3RnYsmSMo02K1q8WHcmgp42mNU7E7OCJ554go8++oj//vuPTZs2ERkZyUsvvUSB2A6b1GjcuDFHjhzh4MGD1luNGjXo0qULBw8eTLZj0tXVFW9vb5ubyDk++kh3JpYqpZfOEanz1FPQt6/eHjJETxcXQgiRO0mVZ5FtXb0KY8fq7cmTwdfX2HxE+lAKBg7UX7t0gTp1jM5IiOwhJkavAXbgAOTLB1u26Kr3IuvYvl2Pvga9LmzPnsbmk904ODjQokULWrRowc2bN1myZEmqn+vl5UWFChVs7suTJw/58uVLcr/I+U6fhunT9faMGZDKWfAi1tixsGSJLtCydKkUQxRCiNxKxv2IbGvECLh/X0+J7dHD6GxEelmxAnbv1usZffyx0dkIkT0oBf366emzbm56ZOKTTxqdlUjo+HFo21Yv1dGxox4dJR6ev78/gwcPNjoNkU0NGqT/Flu00KO6Rdr4+8N77+nt997ThRGFEELkPtKhKLKl33/XV0ZNJpg9W6bE5hRhYTBsmN4eORIef9zYfITILj76CL7+WreFy5fDM88YnZFI6Pp1XRwnKEivb7lwofzfssfX15dbt26lOr5YsWL8999/aX6dHTt2MGPGjDQ/T2RvGzfqiy/Oznp0YroUrMqFBg6EYsXg0iV9HIUQQuQ+MuVZZDsxMboAC+jpYjVrGpuPSD+ffgoXL+oT1KFDjc5GiOxhwQIYM0Zvz5oFbdoYm4+wFRYGrVtDYCA88QSsXy/TKx/k3r17/Pjjj/j4+KQq/vbt2zmqkq7IOBERuiMMYPBgGcn9KNzc9JJDXbrorz17yjIbQgiR20iHosh2vvwSDh+GvHn1CYzIGS5ejJ/iPHUquLsbm48Q2cGWLdC7t94eOVJPexZZh8Wi1xbbt0+v87t5sy4iJh4sICDA6BREDjR1qi5aVaQIfPCB0dlkf6+9pkcn7t+v11X86iujMxJCCJGZpENRZCvXr8Po0Xp78uQM+mDm7Axjx2I2mxkJmB0dcXZ2zoAXEgmNHAnh4VC/PrRvb3Q2QmR9f/0F7dqB2Qyvvw6TJmXCi0r7mCbDh8OaNeDiAuvWyWio1LJI2ViRAQID49vJadPA0zOdXyC2fQRw9vBgbNx2Dm4jHRz0sXzuOZg/H955B8qXNzorIYQQmcWklFJGJ5GZgoOD8fHxISgoCG9vb6PTEWnUrRssWgTVq8PeveDoaHRGIj388QfUq6fXMTpwAKpVy9zXl3YhnhyL7OHcOV0B/cYNaNIENm3SnVYi65gzB/r319tLl0Lnzsbm8yikXdDkOGRvr7wCa9dCw4bw66+ydmJ6iju2LVvq/0e5ibQLQojcTJYEF9nG7t26M9Fk0tOepTMxZ7BY4tcz6tEj8zsThchubt2C5s11Z2LlyrB6tXQmZjWbN8Nbb+ntCROyd2eiEDnB1q26w8vREb74QjoT09snn4CTk277fvnF6GyEEEJkFpnyLLKFxIVYatXKwBezWOD4cSwWC8cBHBwoV64cDlKSM0MsXqxHJXp5wcSJRmcjRNYWFgatWsHp07p40ebNkKkDIqR9fKCDB6FjR32ouneH9983OiMhcrfISD0VF+Dtt6FChQx6odj2EcBStizHT54EyBVtZJkyekT255/DkCHw999y4V8IIXID6VAU2cKcOXDoUCYVYgkPhwoVcABqAWFASEgIefLkyeAXzn3u34dRo/T26NFSHVAIe8xmPdJtzx547DH48UcoXDiTk5D20a5Ll+DFFyEkBBo3hrlzZSSUEEabMQNOndLnGOPGZeALxbaPAOHXr1Mhdju3tJFjxuiLxIcP66/duxudkRBCiIyWsy+XiRwhYSGWSZOkQmZOMmkSXLsGpUvHjx4QQiSllB5Zs349uLrChg2y8H1Wc/8+vPQSXLmi35tVq3SNBiGEcS5d0ssOAEyZAj4+xuaTk+XLF185+4MPIDTU2HyEEEJkPOlQFFmaUtC3LwQF6UIsvXsbnZFIL+fOwfTpenvaNN1JIoRI3qRJeqS2yQTffquroYusIyZGT3M+dEiPgtq8WY8iFY+uQYMGLF68mPDwcKNTEdmMUvDuu7pjq25deP11ozPK+d56C0qW1BdWpk0zOhshhBAZzfApz7Nnz2bq1Klcu3aNypUr88UXX1DLzgJ59+7d4/3332fNmjXcuXOH4sWLM2PGDFq2bJmJWYvMMn06rFunCw7MmyfrseQkQ4dCVBQ0barXhBPJkzZSLFwYP+pj5kxo187QdEQicaNHf/wR3N3hhx+geHGjs8o5qlatytChQ3n77bfp0KEDPXv25JlnnjE6LZENzJunRwo7OsKsWZDDlzHMElxd4eOP9QWWKVP0QIBChYzOKndQShETE4PZbDY6FSFENufo6IiTkxOmVKzbY2iH4vfff8/gwYP56quvqF27NjNmzKBZs2acPHmS/PnzJ4mPioqiadOm5M+fn1WrVlGkSBH+++8/HpNhADnSrl0wYoTenjlTqv/mJL/+Gl9t8bPPZI2xlEgbKX78EXr10tvDh+uOK5G1TJsGX32l27Fly6BmTaMzyllmzJjBp59+yoYNG1i0aBHPPfccpUuXpkePHnTt2pUCsviuSMaBA/FLqUyaBFWrGptPbtK+vT6327NHL1n09ddGZ5TzRUVFcfXqVcLCwoxORQiRQ3h4eFCoUCFcXFzsxpmUUiqTckqidu3a1KxZk1mzZgFgsVgoWrQob7/9NiNHjkwS/9VXXzF16lROnDiB80MuTBQcHIyPjw9BQUF4Z2ppTJEW16/rk7+rV6FLF1iyJBM7nUJDwdMTgDxI0YH0FhOjO4ePHNFTY774wuiMsm67IG1k7rZ/PzRqpJuk11+HRYuywAgbaR9trF4dP2L0s89g0CBD08kwWalduHHjBvPmzWPixImYzWZatmzJO++8w/PPP5/hr52VjoNI2Z07+jzjv/+gdWs90yVTziETtI+h16/jGdvZnRvbyD/+gHr19HE/eBAqVTI6o4xjdLtgsVg4ffo0jo6O+Pv74+LikqpRRUIIkRylFFFRUdy8eROz2UyZMmVwsPMBxLARilFRUfz111+MiivxCjg4ONCkSRP+/PPPZJ+zYcMG6tSpw4ABA1i/fj3+/v507tyZESNG4ChzYXMMsxk6ddKdieXLx4/8EDnD11/rzsS8eTO42mI2J21k7nb2rK4WHBqqlwX45pss0JkobOzZE78m21tvwcCBxuaTG+zbt48FCxawfPly8ufPT7du3bh8+TIvvfQS/fv359NPPzU6RWEwiwW6dtWdiaVK6Qsxcg6Z+erW1SMVV66EYcNg61ajM8q5oqKirBecPTw8jE5HCJEDuLu74+zszH///UdUVBRubm4pxhrWoXjr1i3MZnOSqSoFChTgxIkTyT7n3Llz/Prrr3Tp0oXNmzdz5swZ+vfvT3R0NGPHjk32OZGRkURGRlq/Dw4OTr8fQmSIsWNh+3bIk0evfRN7sTfzODvD0KGYzWbeBsyOjg892kvYuns3fi248eN1RUCRPGkjc68bN6B5c7h5U4/UXr1aryObJUj7COiiUq1bQ0SEruw8Y4Z0WmSUGzdusGTJEhYsWMDp06dp1aoVy5Yto1mzZtZRON26daN58+bSoSj4+GNdFMnVVZ9DZuqKH7HtI4CzhwdD47ZzYRsJ+r1Ytw5++gm2bNH/10TGsTeCSAgh0iq1bYrhRVnSwmKxkD9/fubNm4ejoyPVq1fn8uXLTJ06NcUPy5MnT+bDDz/M5EzFw9q0CSZO1Ntffw3lyhmQhIsLTJ2KI/CxAS+fk334Idy+rUee9u1rdDY5j7SR2V9ICLRsCWfOQIkS+oOxl5fRWSUg7SN37uj3KK7Dd9kyKRiWkR5//HGeeOIJevToQbdu3fD3908SU6lSJWrK4pW53rZtes0+gNmzDVg3MbZ9BHABpsZu51alSul1f6dP1/2sTZqAU7b65CmEEOJBDLuU4efnh6OjI9evX7e5//r16xQsWDDZ5xQqVIgnn3zSZupeuXLluHbtGlFRUck+Z9SoUQQFBVlvFy9eTL8fQqSrW7fgjTf09ltvwWuvGZuPSF/Hj+sTfNBrjclJpX3SRuY+UVHw6qvw11/g56eniKXwVguDREbCK6/AyZNQtChs3GjAKPpcZtu2bRw/fpxhw4Yl25kI4O3tzfbt2zM5M5GVXL0KnTvrKc/dukGPHkZnJEDPSvH1hX//hQULjM5G5BYmk4l169alKnbcuHFUqVLFbkzDhg0ZlM0WSQ4MDMRkMnHw4EGjU3kkO3bswGQyce/ePaNTESkwrEPRxcWF6tWrs23bNut9FouFbdu2UadOnWSfU69ePc6cOYPFYrHed+rUKbvVZ1xdXfH29ra5iaxp1Cg98qNSJTB01pLFAoGBWM6dI/DcOQIDA21+58TDGTxYF2Rp1QpeeMHobLI+aSNzF4tFfwD+6Sfw8NCjtZ980uiskpGL20eloHdv2LlTjxrduBEKFzY6q5xv7NixyX6QCA4OzpRCLCJ7GDJELxdRqZK+eGnIEgSx7SOBgVhiYggMDMxVbWRy8uaFMWP09ujRcP++sfmIrOPmzZv069ePYsWK4erqSsGCBWnWrBm7d++2xqSlYzChq1ev0qJFi3TLdc2aNUyYMCHd9vewFi5cyGOpXMehaNGiXL16lQoVKmRsUiLXM3SxhcGDBzN//nwWLVrE8ePH6devH6GhoXTv3h2AN954w6YgQb9+/bhz5w4DBw7k1KlTbNq0iUmTJjFgwACjfgSRTvbt00UHAL78Uq99Y5jwcChZEocnnuDpJ56gZMmShIeHG5hQ9rd5s14/x9kZpk0zOpvsQ9rI3GPECFi6VI/cXb0aatUyOqMU5OL28cMPYckSPb151aqcXbU0K9m5c2eyI6wjIiLYtWuXARmJrGbHDr30gMmkR8EZVpcitn2kZEnC79yhZMmSuaqNTEm/flC6NFy/DlOmGJ2NyCpeffVV/vnnHxYtWsSpU6fYsGEDDRs25Pbt24+874IFC+Kajh8mfX198cpS68/YFxUVhaOjIwULFsRJpoSJDGZoh2LHjh359NNPGTNmDFWqVOHgwYNs2bLFWoTgwoULXL161RpftGhRtm7dyv79+6lUqRLvvPMOAwcOZOTIkUb9CCIdmM0wYIAe/fHGG1CvntEZifQUFQXvvqu3Bw6EMmWMzSc7kTYyd5g+PX5U9jffyML1WdHixbpDEWDOHBllnRkOHz7M4cOHUUpx7Ngx6/eHDx/mn3/+4ZtvvqFIkSJGpykMFh2tzyFBr81crZqx+YikXFzgk0/09rRpcOmSsfkI4927d49du3bxySef0KhRI4oXL06tWrUYNWoUrVu3BqBEiRIAtG3bFpPJZP0eYM6cOTzxxBO4uLhQtmxZlixZYrP/xCMbL126RKdOnfD19SVPnjzUqFGDvXv32jxnyZIllChRAh8fH1577TXuJxhOm3jK8927d3njjTfImzcvHh4etGjRgtOnT1sfjxtJuHHjRsqWLYuHhwft2rUjLCyMRYsWUaJECfLmzcs777yD2Wy2Pi8yMpKhQ4dSpEgR8uTJQ+3atdmxYwegp/52796doKAgTCYTJpOJcePGWY/VhAkTeOONN/D29qZPnz7JTnn+999/eemll/D29sbLy4v69etz9uzZFN+no0eP0qJFCzw9PSlQoABdu3bl1q1bNsflnXfeYfjw4fj6+lKwYEFrTgCdO3emY8eONvuMjo7Gz8+PxYsXA3r21eTJkylZsiTu7u5UrlyZVatWpZgTwOrVq3n66adxdXWlRIkSTEs0WiXueHTq1Ik8efJQpEgRZsetuxXr3r179OrVC39/f7y9vXn++ec5dOiQ3dcVKVC5TFBQkAJUUFCQ0amIWHPnKgVKeXsrde2a0dkopUJCdEKgPEABKiQkxOissq3p0/XhzJ9fqXv3jM4medIuxJNjkbmWLrU2N+qTT4zOJhVyYfu4fbtSzs76xx4xwuhsjGFEu2AymZSDg4NycHBQJpMpyc3Dw0N98803mZaPUtI+ZkXTpum/zXz5lLp92+BkErSPIdevK3JJG5kaFotSzz6rD09AgNHZpC+j24Xw8HB17NgxFR4ebr3PYtG/jpl9s1hSl3N0dLTy9PRUgwYNUhEREcnG3LhxQwFqwYIF6urVq+rGjRtKKaXWrFmjnJ2d1ezZs9XJkyfVtGnTlKOjo/r111+tzwXU2rVrlVJK3b9/X5UqVUrVr19f7dq1S50+fVp9//336o8//lBKKTV27Fjl6empXnnlFXXkyBH122+/qYIFC6r33nvPur8GDRqogQMHWr9v3bq1KleunPrtt9/UwYMHVbNmzVTp0qVVVFSUUkqpBQsWKGdnZ9W0aVP1999/q507d6p8+fKpF154QXXo0EH9+++/6ocfflAuLi5q+fLl1v326tVL1a1bV/3222/qzJkzaurUqcrV1VWdOnVKRUZGqhkzZihvb2919epVdfXqVXX//n2llFLFixdX3t7e6tNPP1VnzpxRZ86cUefPn1eA+ueff5RSSl26dEn5+vqqV155Re3fv1+dPHlS/e9//1MnTpxI9vjfvXtX+fv7q1GjRqnjx4+rv//+WzVt2lQ1atTI5rh4e3urcePGqVOnTqlFixYpk8mkfvrpJ6WUUhs3blTu7u7WPJVS6ocfflDu7u4qODhYKaXURx99pJ566im1ZcsWdfbsWbVgwQLl6uqqduzYoZRSavv27QpQd+/eVUopdeDAAeXg4KDGjx+vTp48qRYsWKDc3d3VggULrK9RvHhx5eXlpSZPnqxOnjypPv/8c+Xo6GjNSymlmjRpolq1aqX279+vTp06pYYMGaLy5cunbhv+jyTrSK5tSY50KApD3bqllK+vPsGYMcPobGLlwg/MGeXGDaV8fPTh/Ppro7NJmbQL8eRYZJ6tW5VyctJ/HwMHpv5E3FC5rH08dkypxx7TP3KHDkqZzUZnZAwj2oXAwEB1/vx5ZTKZ1P79+1VgYKD1duXKFRUTE5NpucSR9jFruXJFKS8v/fc5f77R2SjpUHyAvXv14TGZlPr7b6OzST9GtwvJfehP8KuYqbe0/KqvWrVK5c2bV7m5uam6deuqUaNGqUOHDtnEJOwYjFO3bl3Vu3dvm/vat2+vWrZsmezz5s6dq7y8vFLsKBo7dqzy8PCwdnAppdSwYcNU7dq1rd8n7FA8deqUAtTu3butj9+6dUu5u7urFStWKKV0hyKgzpw5Y4158803lYeHh03nWrNmzdSbb76plFLqv//+U46Ojury5cs2+TVu3FiNGjXKul8fH58kP0Px4sXVyy+/bHNf4g7FUaNGqZIlS1o7PR9kwoQJ6oUXXrC57+LFiwpQJ0+etB6XZ5991iamZs2aakTs1dfo6Gjl5+enFi9ebH28U6dOqmPHjkoppSIiIpSHh4e1czdOz549VadOnZRSSTsUO3furJo2bWoTP2zYMFW+fHmb49G8eXObmI4dO6oWLVoopZTatWuX8vb2TtKZ/cQTT6i5c+c+4MjkHqntUDR0yrMQ77+vC7FUrBg/ZUXkHKNHQ1AQVK2qqy4KIbT9+3W14JgY6NRJT3s2pIiASNGNG/Dii3DvHtSpAwsXgoOcNWWa4sWLU6JECSwWCzVq1KB48eLWW6FChWyq2YvcadgwXeSjVi2p6pwd1Kql/98pBUOH6q8i93r11Ve5cuUKGzZsoHnz5uzYsYNq1aqxcOFCu887fvw49RKtj1WvXj2OHz+ebPzBgwepWrUqvr6+Ke6zRIkSNmskFipUiBs3bqT4+k5OTtSuXdt6X758+ShbtqxNDh4eHjzxxBPW7wsUKECJEiXw9PS0uS/udY4cOYLZbObJJ5/E09PTetu5c6fdaclxatSoYffxgwcPUr9+fZydnR+4L4BDhw6xfft2m1yeeuopAJt8KiVaUDrhsXNycqJDhw4sXboUgNDQUNavX0+XLl0AOHPmDGFhYTRt2tTmdRYvXpziz5zS+3/69Gmb6eOJC1jWqVPH+v4cOnSIkJAQ8uXLZ/O658+fT9WxFrZklU5hmAMHYN48vT1rli5GIHKOQ4dg/ny9PXOmLmQghIDTp6FlSwgNhSZNpKMqKwoPh9at4fx5KFUK1q8Hd3ejs8o9NmzYQIsWLXB2dmbDhg12Y+PW20qNOXPmMGfOHAIDAwF4+umnGTNmTLpWAxWZ47ffdCErk0lXdZY2NHuYNAnWrIFff9UF+1580eiMciYPDwgJMeZ108LNzY2mTZvStGlTRo8eTa9evRg7dizd0nEUgnsq/nkn7mQzmUyPXJ09uX3ae52QkBAcHR3566+/klwwS9gJmZI8efLYfTw1xyGhkJAQWrVqxSdxC6AmUKhQIev2g45dly5daNCgATdu3ODnn3/G3d2d5rGLhYfE/pJu2rQpyZrI6VlUJ7GQkBAKFSpkXZ8yodRW0RbxpAtHGMJigbfe0lcnu3SB554zOiORnpSCQYP0+9yhA9Svb3RGQmQNV69Cs2Zw6xZUr64/WLm4GJ2VSMhiga5dYe9eyJtXf+j19zc6q9zl5Zdf5tq1a+TPn5+XX345xTiTyWQzIuFBHn/8cT7++GPKlCmDUopFixbRpk0b/vnnH55++ul0yFxkhpgYfQ4J0KcPPGBgjshCSpTQBfqmTNEjTJs1kwEFGcFkggf0L2VJ5cuXtymm4uzsnKSNL1euHLt37yYgIMB63+7duylfvnyy+6xUqRJff/01d+7csTtKMbXKlStHTEwMe/fupW7dugDcvn2bkydPpphDalStWhWz2cyNGzeon8IHJxcXlzT9z0uoUqVKLFq0iOjo6FSNUqxWrRqrV6+mRIkSj1Qpum7duhQtWpTvv/+eH3/8kfbt21tfv3z58ri6unLhwgUaNGiQqv3Fvf8J7d69myeffNKmI3bPnj02MXv27KFcuXLWn+3atWs4OTnZFPsRD0eacGGIhQv1hzUvL5g61ehsEnFygv79MZvN9AJiHB0fqSHNjdasgR07wM1NnzQKIfT0/xYt9Ki30qV1R1WCGTbZQy5oH0eMgNWrdUfvunVQtqzRGeU+CUc3POookYRatWpl8/3EiROZM2cOe/bskQ7FbGT2bDhyBHx9YeJEo7NJILZ9BHByc6N/3HYOayMf1ahR8M03cPy4nsnSr5/RGYnMdvv2bdq3b0+PHj2oVKkSXl5eHDhwgClTptCmTRtrXIkSJdi2bRv16tXD1dWVvHnzMmzYMDp06EDVqlVp0qQJP/zwA2vWrOGXX35J9rU6derEpEmTePnll5k8eTKFChXin3/+oXDhwkmmxaZGmTJlaNOmDb1792bu3Ll4eXkxcuRIihQpYpN7Wj355JN06dKFN954g2nTplG1alVu3rzJtm3bqFSpEi+++CIlSpQgJCSEbdu2UblyZTw8PPBI5bDQt956iy+++ILXXnuNUaNG4ePjw549e6hVqxZlkznRGTBgAPPnz6dTp07WKs5nzpxh+fLlfP3112ladqRz58589dVXnDp1iu3bt1vv9/LyYujQobz77rtYLBaeffZZgoKC2L17N97e3jadxnGGDBlCzZo1mTBhAh07duTPP/9k1qxZfPnllzZxu3fvZsqUKbz88sv8/PPPrFy5kk2bNgHQpEkT6tSpw8svv8yUKVN48sknuXLlCps2baJt27YPnD4uEsmcJR2zDqMXzhVK3b2rlL+/Xrx32jSjsxHpLTxcqRIl9Ps7erTR2aSOtAvx5FhkjPBwpRo21H8XBQoodfas0RmJ5MyZE7+4/NKlRmeTdeTEdiEmJkYtW7ZMubi4qH///TdVz8mJxyG7uXZNKW9v/Tcqa+dnX198od9Df3+lsvufk9HtQmoLJ2QlERERauTIkapatWrKx8dHeXh4qLJly6oPPvhAhYWFWeM2bNigSpcurZycnFTx4sWt93/55ZeqVKlSytnZWT355JM2RT+USlrMJTAwUL366qvK29tbeXh4qBo1aqi9e/cqpXRRlsqVK9s8/7PPPrN5vcRVnu/cuaO6du2qfHx8lLu7u2rWrJk6deqU9fHkiqck9zoBAQGqTZs21u+joqLUmDFjVIkSJZSzs7MqVKiQatu2rTp8+LA1pm/fvipfvnwKUGPHjlVK6SIkn332mc2+ExdlUUqpQ4cOqRdeeEF5eHgoLy8vVb9+fXXWzgnpqVOnVNu2bdVjjz2m3N3d1VNPPaUGDRqkLLFVBBMfF6WUatOmjQpIVMr92LFjClDFixe3PjeOxWJRM2bMUGXLllXOzs7K399fNWvWTO3cuVMplbQoi1K6oE/58uWVs7OzKlasmJo6darNPosXL64+/PBD1b59e+Xh4aEKFiyoZs6caRMTHBys3n77bVW4cGHl7OysihYtqrp06aIuXLiQ4vHIbVLbtpiUyl1L4gYHB+Pj40NQUBDe3t5Gp5MrvfsuzJgB5crpdfZSuTasyCYmTdLFdooUgZMns8eUC2kX4smxSH9mM7z2GqxapUck7typCxWJrOXHH+Gll/SU5wkT4IMPjM4o6zCyXXjnnXcoXbo077zzjs39s2bN4syZM8yYMSNN+zty5Ah16tQhIiICT09PvvvuO1q2bJlsbGRkJJGRkdbvg4ODKVq0qLSPBureXc9yqVED9uyR9Zmzq+hoqFABTp3SIxYnTTI6o4dn9HlTREQE58+fp2TJkri5uWX66wuR1ZQoUYJBgwYxaNAgo1PJ1lLbtsgSxiJTHTsGX3yht2fOzKKdiUrBzZuoGze4eeMGN2/eJJf1uz+0K1fiTwo/+SR7dCYKkZHi1hNdtUq3d+vWZfPOxBzaPh46pNd7tVh0Rfr33zc6IxFn9erVSSo6gl6XadWqVWneX9myZTl48CB79+6lX79+BAQEcOzYsWRjJ0+ejI+Pj/VWtGjRNL+eSD979ujORNDF/LJcZ2Js+8jNmyiLhZs3b+aYNjK9OTvHL4nz2Wdw4YKx+QghhHg40qEoMo1S8M47erTOyy9D06ZGZ5SCsDDInx9TgQKUKFCA/PnzExYWZnRW2cKoUbpybZ060Lmz0dkIYbyPP9YffAGWLIHnnzc2n0eWA9vHS5d0pdGQEP3+zJ2rF7QXWcPt27fx8fFJcr+3tze3bt1K8/5cXFwoXbo01atXZ/LkyVSuXJmZM2cmGztq1CiCgoKst4sXL6b59UT6MJvjC7F07w61axubT7Ji20fy5yfs1i3y58+fI9rIjNK6NTRoABERchFHCCGyK+lQFJlm7VrYtg1cXWHaNKOzEelt3z5YvFhvz5wpH8iFWLgQ3ntPb8+YAR07GpmNSM79+3qa8+XLUL58fDEWkXWULl2aLVu2JLn/xx9/pFSpUo+8f4vFYjOtOSFXV1e8vb1tbsIY33wDf/0FPj4webLR2Yj0YDLBp5/q7W+/hQMHjM1HCJEzBAYGynTnTJTmDsWElXkSmzt37iMlI3Ku8HAYPFhvDx8O6fAZQGQhSsHAgXo7IABq1jQ2HyMFBATw22+/GZ2GMNjmzdCrl94eMSL+70NkHTExupP30CEoUAA2bYLHHjM6K5HY4MGDGT58OGPHjmXnzp3s3LmTMWPGMHLkSN5999007WvUqFH89ttvBAYGcuTIEUaNGsWOHTvo0qVLBmUv0sOdO/EXZz78UP+9ipyhRg14/XW9PWSIPp8UQgiRfaS5Q7F58+YMGzaM6Oho6323bt2iVatWjBw5Ml2TEznH1Knw339QtCjIr0nO8913em2jPHmy98La6SEoKIgmTZpQpkwZJk2axOXLl41OSWSyvXuhfXs9Re+NN2Q0TVakFLz9ti7E4u4OP/wAJUoYnZVITo8ePZg2bRrffPMNjRo1olGjRnz77bfMmTOH3r17p2lfN27c4I033qBs2bI0btyY/fv3s3XrVppm2TVYBMDo0XD7ti7iMWCA0dmI9DZxIri5wW+/wYYNRmcjhBAiLR5qhOLatWupWbMmx44dY9OmTVSoUIHg4GAOHjyYASmK7O6//+I/UH/6KXh4GJuPSF+hoXoEFug1cAoXNjYfo61bt47Lly/Tr18/vv/+e0qUKEGLFi1YtWqVzYUYkTOdOqXX4wsLg+bN4euvZfp/VjRtGnz1lX5vvvsud4+qzg769evHpUuXuH79OsHBwZw7d4433ngjzfv55ptvCAwMJDIykhs3bvDLL79IZ2IWd/Cg/lsFXdTPycnQdEQGKFYM4gYbDx+uK0ALIYTIHtLcoVi3bl0OHjxIhQoVqFatGm3btuXdd99lx44dFC9ePCNyFNnc0KF6weWGDfWoHZGzfPKJXn+sZMn4E8Lczt/fn8GDB3Po0CH27t1L6dKl6dq1K4ULF+bdd9/l9OnTRqcoMsC1a9CsmR5JU6MGrFyZRSvZ53KrV8OwYXp72jRdJExkD/7+/nh6ehqdhsgkSulCLBaLXp6gYUOjMxIZZeRIXc/m1Kn4DmQhhBBZ30MVZTl16hQHDhzg8ccfx8nJiZMnT0oFM5Gs7dth1SpwcIDPP5eROjnNf//p6eygv7q5GZtPVnP16lV+/vlnfv75ZxwdHWnZsiVHjhyhfPnyfPbZZ0anJ9JRcDC0bAmBgfDEE3o9Pun3yHr27Ilfr2vAAJA1u7OHVatW0aFDB5555hmqVatmcxM519KlsHu3ntkSV7xD5Eze3np9TNBf790zNB0hhBCplOYOxY8//pg6derQtGlTjh49yr59+/jnn3+oVKkSf/75Z0bkKLKpmJj4QgT9+kHFisbmk2pOThAQgPn11+n0+usEBATgJHNskjV8ePzo01deMTqbrCE6OprVq1fz0ksvUbx4cVauXMmgQYO4cuUKixYt4pdffmHFihWMHz/e6FRFOomKgldfhX/+AX9/2LpVj7TIkbJx+3juHLRurdusF1/UlbflIlfW9/nnn9O9e3cKFCjAP//8Q61atciXLx/nzp2jRYsWRqcnMsj9+/ocA+CDD+Dxx43NJ1Vi20cCAnBycyMgICBbtZFG69ULypXTo/xz+3rcQgiRbag0KliwoNq8ebPNfVFRUWro0KHKxcUlrbvLdEFBQQpQQUFBRqeS482erRQo5eur1O3bRmcj0tvOnfr9dXBQ6uBBo7N5NOnZLuTLl0/lzZtX9e/fX/3zzz/Jxty9e1eVKFHikV8rI0gbmTZms1Kvv67/FvLkUWr/fqMzEsm5c0epp57S71PVqkrdv290RtmLke1C2bJl1XfffaeUUsrT01OdPXtWKaXU6NGj1YABAzI1F2kfM8+wYfrvtXRppSIijM5GZJaNG/X77uKi1LlzRmeTOka3C+Hh4erYsWMqPDzckNc32oIFC5SPj0+67e/8+fMKSPEcPrP3kxpjx45V+fPnV4Bau3Zthr+ekbZv364Adffu3VQ/p0GDBmrgwIF2Y4oXL64+++yzh84r8fud2jwf9LqZ+XuUWGrbljSPUDxy5EiSK8LOzs5MnTqVn3766VH6NkUOcueOrsoHMH48+Poam49IX2Zz/FTB3r2hcmVD08lSPvvsM65cucLs2bOpUqVKsjGPPfYY58+fz9zERIYYNQq+/VYPTFm1Sq+dKLKWqCg9gvrECT3KaeNGmY6enVy4cIG6desC4O7uzv379wHo2rUry5YtMzI1kUFOntQjiEF/dXU1MhuRmVq2hMaNdbv93ntGZyMy2rVr13j77bcpVaoUrq6uFC1alFatWrFt2zajU0uTbt268XKiBZmLFi3K1atXqVChQoa+9vHjx/nwww+ZO3cuV69elZH7WUTdunW5evUqPj4+ACxcuJDHHnsszfvJrN+jR5HmDkU/P78UH2vQoMEjJSNyjjFjdKdixYrw5ptGZ5NGSkFoKCokhNCQEEJDQ1FKGZ1VlrJggZ7e6eMDEyYYnU3W0rVrV9xkMclc4fPPYcoUvf3117qqc46XzdpHpfQ0uh07wMtLr22Z2yvRZzcFCxbkzp07ABQrVow9e/YAcP78+Sz9uycejlJ6uZzoaL00wYsvGp1RGsS2j4SGoiwWQkNDs3wbmdWYTHq9TJMJli+HvXuNzkhklMDAQKpXr86vv/7K1KlTOXLkCFu2bKFRo0YMGDDA6PQemaOjIwULFszwJQ/Onj0LQJs2bShYsCCuyVyBiYqKytAcRFIuLi4ULFgQ0yOurZNZv0eP4qGKsghhz5EjMGeO3p45U4/cyVbCwsDTE5OXF/m9vPD09JSiQwkEBcH77+vtsWP1mnFC5DYrVsSP0p00SS+blStks/Zx/HhYsgQcHXXV7UqVjM5IpNXzzz/Phg0bAOjevTvvvvsuTZs2pWPHjrRt29bg7ER627BBr0Pr4hI/SjHbiG0f8fQk7NYtPD09s3wbmRVVqRL/P3XwYN1PK3Ke/v37YzKZ2LdvH6+++ipPPvkkTz/9NIMHD7ZeOAKYPn06FStWJE+ePBQtWpT+/fsTEhJid98//PADNWvWxM3NDT8/P5v/FSaTiXXr1tnEP/bYYyxcuDDZfZnNZnr27EnJkiVxd3enbNmyzJw50/r4uHHjWLRoEevXr8dkMmEymdixYweBgYGYTCYOHjxojd25cye1atXC1dWVQoUKMXLkSGJiYqyPN2zYkHfeeYfhw4fj6+tLwYIFGTduXIo/57hx42jVqhUADg4O1s6ruBGTEydOpHDhwpQtWxbQM02ff/553N3dyZcvH3369LE5lnHPmzRpEgUKFOCxxx5j/PjxxMTEMGzYMHx9fXn88cdZsGCB3eNvsViYMmUKpUuXxtXVlWLFijFx4kRA/09/6623bOJv3ryJi4uLdWRqZGQkI0aMoGjRori6ulK6dGm++eabZF/r9u3bdOrUiSJFiuDh4UHFihWTnb0QExPDW2+9hY+PD35+fowePdruxZ579+7Rq1cv/P398fb25vnnn+fQoUN2f+6EduzYgclk4t69e+zYsYPu3bsTFBRk/R1J+L6GhYXRo0cPvLy8KFasGPPmzbM+lvj3KLmRjuvWrbPpuBw3bhxVqlThf//7H8WKFcPT05P+/ftjNpuZMmUKBQsWJH/+/Nb35FFlt64ekcXFXVm2WHSRgkaNjM5IpLePPoIbN6BsWV0lVYjcZscO6NpVt3cDBsDIkUZnJJKzZAnEna99+SU0a2ZoOuIhzZs3D4vFAsCAAQPIly8ff/zxB61bt+bNbDcFQtgTEQHvvqu3hwyB0qWNzUcY56OP9IW7P/6A1auhXTujM8qeQkNDU3zM0dHRZkaNvVgHBwfc3d3txubJkyfVed25c4ctW7YwceLEZJ+XsMPEwcGBzz//nJIlS3Lu3Dn69+/P8OHD+fLLL5Pd96ZNm2jbti3vv/8+ixcvJioqis2bN6c6t8QsFguPP/44K1eutP7/6dOnD4UKFaJDhw4MHTqU48ePExwcbO1o8/X15cqVKzb7uXz5Mi1btqRbt24sXryYEydO0Lt3b9zc3Gw6lxYtWsTgwYPZu3cvf/75J926daNevXo0bdo0SW5Dhw6lRIkSdO/enatXr9o8tm3bNry9vfn5558B/Z41a9aMOnXqsH//fm7cuEGvXr146623bDpTf/31Vx5//HF+++03du/eTc+ePfnjjz947rnn2Lt3L99//z1vvvkmTZs25fEUqmWNGjWK+fPn89lnn/Hss89y9epVTpw4AWB9zWnTpllHU3777bcUKVKE559/HoA33niDP//8k88//5zKlStz/vx5bt26lexrRUREUL16dUaMGIG3tzebNm2ia9euPPHEE9SqVcvmuPbs2ZN9+/Zx4MAB+vTpQ7Fixejdu3ey+23fvj3u7u78+OOP+Pj4MHfuXBo3bsypU6fwTeNabnXr1mXGjBmMGTOGkydPAuCZYO2dadOmMWHCBN577z1WrVpFv379aNCggbUj+GGcPXuWH3/8kS1btnD27FnatWvHuXPnePLJJ9m5cyd//PEHPXr0oEmTJtSuXfuhXwdIe1GW7M7ohXNzulWr9GLKbm5KnT9vdDYPKSRE/xCgPEABKiQkxOissoRTp5RydtaHZ9Mmo7NJP9IuxJNjYd/hw0r5+Oi/gVdeUSomxuiMMlk2aR+3b49vq0aMMDqb7E/aBU2OQ8aaMEH/zRYpkk0LJyVoH0OuX1dk4TYyOxgzRh/OUqWUiow0OpuUGd0u2CucEPc7mNytZcuWNrEeHh4pxjZo0MAm1s/PL0lMWuzdu1cBas2aNWn+eVeuXKny5ctn/T5xUZY6deqoLl26pPh8kilc4uPjoxYsWKCUSl0RjAEDBqhXX33V+n1AQIBq06aNTUzi/bz33nuqbNmyymKxWGNmz56tPD09ldlsVkrp4iHPPvuszX5q1qypRtg5kVm7dm2S4x8QEKAKFCigIhP84cybN0/lzZvXpj3atGmTcnBwUNeuXbM+r3jx4tZ8lNKF0erXr2/9PiYmRuXJk0ctW7Ys2XyCg4OVq6urmj9/frKPh4eHq7x586rvv//eel+lSpXUuHHjlFJKnTx5UgHq559/Tvb5qSl28uKLL6ohQ4ZYv2/QoIEqV66czbEfMWKEKleunPX7hMVRdu3apby9vVVEoopgTzzxhJo7d26yr/mgoiwpFQ8qXry4ev31163fWywWlT9/fjVnzpxk95vcfhL/DowdO1Z5eHio4OBg633NmjVTJUqUSPLeTp48OdmfR6kMLMoiRErCw/UVZYBhw6BECUPTERlgyBC9rlGLFnrhbCFykwsX9DqJQUHw7LO6GIujo9FZicROnIC2bXVb1aGDnpIusre7d+/y6aef0rNnT3r27Mm0adOs6yqKnOHChfi/1U8/lcJJQn+WKFgQzp2D2bONzkakJ5WGeey//PILjRs3pkiRInh5edG1a1du376d4lICBw8epHHjxumVKgCzZ8+mevXq+Pv74+npybx587hw4UKa9nH8+HHq1KljMzW1Xr16hISEcOnSJet9lRKtzVKoUCFu3LiR5pwrVqyIi4uLzetXrlzZZkRovXr1sFgs1lFzAE8//TQODvFdRAUKFKBixYrW7x0dHcmXL1+KOR0/fpzIyMgU3wM3Nze6du3K//73PwD+/vtvjh49Srdu3QD9/jk6Oqa6NofZbGbChAlUrFgRX19fPD092bp1a5L355lnnrE59nXq1OH06dOYzeYk+zx06BAhISHky5fPunSFp6cn58+ft65ZmZ4Svucmk4mCBQs+1HueUIkSJfDy8rJ+X6BAAcqXL5/kvX3U1wGZ8izS0bRp8N9/uormiBFGZyPS208/wQ8/6DUxp083OhshMtedO7oz8coVKF9er/OVYPaPyCJu3NAXO+7dgzp1YOFCcJBLp9nab7/9RuvWrfH29qZGbBn1zz//nPHjx/PDDz/w3HPPGZyhSA9Dh+oL0w0aQMeORmcjsgJPT134r3dv/TUgANI40zDXs7fWoGOiK6L2OhYcEv0jDQwMfKS8ypQpg8lksk6DTUlgYCAvvfQS/fr1Y+LEifj6+vL777/Ts2dPoqKi8PDwSPIc9wecnJlMpiQdmtHR0SnGL1++nKFDhzJt2jTq1KmDl5cXU6dOZW8GVQxydnZOkm/csh9pkZYp6A96/bTk9KDjD3rac5UqVbh06RILFizg+eefp3jx4ql+fkJTp05l5syZzJgxw7rW5qBBgx6pEE1ISAiFChVix44dSR57mErND5KW4+vg4JCq399HfR/TQk6zRbq4fBkmT9bbn3wCD9mGiSwqOjp+XaMBA+Cpp4zNR4jMFB4OrVvD8eNQpAhs2QJ58xqdlUgsPBzatIHz56FUKVi/Xjp9c4IBAwbQoUMHzp8/z5o1a1izZg3nzp3jtddeyxGVQIVel3blSt35//nnusKvEADdu0OFCnD3rl5XUaRNnjx5UrwlXD/xQbGJO3mSi0kLX19fmjVrxuzZs5Ndj/HevXsA/PXXX1gsFqZNm8YzzzzDk08+mWRtwsQqVapkLe6RHH9/f5v1Bk+fPm23cNLu3bupW7cu/fv3p2rVqpQuXTrJKDUXF5dkR7olVK5cOf7880+bzqDdu3fj5eWV4lqE6alcuXIcOnTI5njv3r0bBweHR1qrL7EyZcrg7u5u9z2oWLEiNWrUYP78+Xz33Xf06NHD5jGLxcLOnTtT9Xq7d++mTZs2vP7661SuXJlSpUpx6tSpJHGJO4D37NlDmTJlknSsA1SrVo1r167h5ORE6dKlbW5+fn6pyiux1PyOpIa/vz/379+3eR8TFv4xgnQoinQxcqQublevHnTqZHQ2Ir199RUcOwZ+frqysxC5RUyMbtN27wYfH92ZWLSo0VmJxCwWXShnzx7d2bt5s1SgzynOnDnDkCFDbE76HR0dGTx4MGfOnDEwM5EeYmJ0MT+Avn2lEruw5eiop8ADzJoF8iefc8yePRuz2UytWrVYvXo1p0+f5vjx43z++efUqVMHgNKlSxMdHc0XX3zBuXPnWLJkCV999ZXd/Y4dO5Zly5YxduxYjh8/zpEjR/jkk0+sjz///PPMmjWLf/75hwMHDtC3b98kI7cSKlOmDAcOHGDr1q2cOnWK0aNHs3//fpuYEiVKcPjwYU6ePMmtW7eSHTHWv39/Ll68yNtvv82JEydYv349Y8eOZfDgwUlGgGaELl264ObmRkBAAEePHmX79u28/fbbdO3alQIFCqTb67i5uTFixAiGDx/O4sWLOXv2LHv27ElSpblXr158/PHHKKVsqnCXKFGCgIAAevTowbp16zh//jw7duxgxYoVyb5emTJl+Pnnn/njjz84fvw4b775JtevX08Sd+HCBQYPHszJkydZtmwZX3zxBQPj/vkk0qRJE+rUqcPLL7/MTz/9RGBgIH/88Qfvv/8+Bw4ceKjjUqJECUJCQti2bRu3bt2y24ltT+3atfHw8OC9997j7NmzfPfddylWKM8s0qEoHtmff+q1xEwmmDkzB1xZdnSEdu0wt21Lm7ZtadeuXbJXL3KL27fjOxEnTJCRWSL3iKvivH49uLrqac4VKhidlcGyaPs4cqSuBOriAuvW6Sr0ImeoVq0ax48fT3J/3HpQInubPx8OH9bnFuPHG53NI4ptH2nXDkcXF9q1a5dl2sjsrFkzfYuO1m29yBlKlSrF33//TaNGjRgyZAgVKlSgadOmbNu2jTlz5gBQuXJlpk+fzieffEKFChVYunQpk+OmxKWgYcOGrFy5kg0bNlClShWef/559u3bZ3182rRpFC1alPr169O5c2eGDh2a7NTpOG+++SavvPIKHTt2pHbt2ty+fZv+/fvbxPTu3ZuyZctSo0YN/P392b17d5L9FClShM2bN7Nv3z4qV65M37596dmzJx988EFaDttD8/DwYOvWrdy5c4eaNWvSrl07GjduzKxZs9L9tUaPHs2QIUMYM2YM5cqVo2PHjkmm1Hfq1AknJyc6deqUZLTsnDlzaNeuHf379+epp56id+/eKVYh/+CDD6hWrRrNmjWjYcOGFCxYkJdffjlJ3BtvvEF4eDi1atViwIABDBw4kD59+iS7T5PJxObNm3nuuefo3r07Tz75JK+99hr//fffQ3e+1q1bl759+9KxY0f8/f2ZMmXKQ+3H19eXb7/9ls2bN1OxYkWWLVtmUyXcEHZLtmSSWbNmqeLFiytXV1dVq1YttXfv3lQ9b9myZQpIUlXJHqMrceU0ZrNSNWvqKmw9ehidjcgIAwbo97dSpZxb0TYrtwuZ2T4qlbWPRWYbN07/7js4KPUQhQhFJpkzx1pYVX37rdHZ5ExGtgvLly9XxYoVU1OnTlW7du1Su3btUlOnTlUlSpRQy5cvV4cOHbLeMpq0j+nr9m2lfH313+6sWUZnI7KyI0f0/2JQ6vffjc7GltHtQmorsQqRlZw/f145ODiov/76y+hURApS27YYXpTl+++/Z/DgwXz11VfUrl2bGTNm0KxZM06ePEn+/PlTfF5gYCBDhw6lfv36mZitSGzJEti/H7y8YOJEo7MR6e3oUT3dGWDGDKlom9mkfTTOvHkQd8Fv9mxdNVhkPT/+qEeRgh7d1KWLsfmI9Ncpdh2V4cOHJ/tY3AL7JpMpXdYnEpln7Fhd8KpiRXjzTaOzEVlZhQrQowd8/TUMGaJnR2X7GVFC5ELR0dHcvn2bDz74gGeeeYZq1aoZnZJ4RIZPeZ4+fTq9e/eme/fulC9fnq+++goPDw9rKfHkmM1munTpwocffkipUqUyMVuR0P378VMPRo+GggWNzUekL6V0IRazGV55BRo1Mjqj3EfaR2OsXw/9+unt0aP1ul4i6zl0CDp00OsndusGmTRrSGSy8+fP272dO3fO+lVkH0ePQuysRmbOBCfDhziIrG7CBF30ce9e+P57o7MRQjyM3bt3U6hQIfbv3//A9TBF9mDov++oqCj++usvRo0aZb3PwcGBJk2a8Oeff6b4vPHjx5M/f3569uzJrl277L5GZGQkkZGR1u+Dg4MfPXEBwKRJcO0alC4N77xjdDbpKDQUPD0ByAOEocvHp7WCWXb3ww/wyy967bipU43OJvfJjPYRpI1MbPdueO013UnVqxd8+KHRGWUxWaR9vHQJXnwRQkLg+edh7lwZrZJTFS9e3OgURDpTShdiMZvh1Vdz0AXLBO1j6PXreMautZUbzyEzQsGCMGIEjBmjBzS8/DIkWnpNCJHFNWzY0KbStcj+DO1QvHXrFmazOcnilgUKFODEiRPJPuf333/nm2++SXV57MmTJ/OhfCJMd2fPwvTpenv6dN3pJHKOyEgYPFhvDx4MMtAt82VG+wjSRib077/w0ksQEQGtWunRM9JJlfXcv6/fp8uXoVy5+GIsImc7duwYFy5cICoqyub+1q1bG5SReFhr1sCvv+rOoLgKvkKkxuDBeime//6DL76AYcOMzkgIIXK3bDXB4P79+3Tt2pX58+fj5+eXqueMGjWKwXE9I+jRN0WLFs2oFHONwYMhKgpeeEF/sBM5y8yZutO4UCFIMEBOZGEP0z6CtJFxLl6E5s3h3j2oUweWL5cpeFlRTAx07KinO+fPD5s3w2OPGZ2VyEjnzp2jbdu2HDlyxLpeIugqjICsm5jNhIfD0KF6e9gwKFHC0HRENpMnj16zvXv3+K9pOOURQgiRzgz9uOTn54ejoyPXr1+3uf/69esUTGZBvrNnzxIYGEirVq2s91ksFgCcnJw4efIkTzzxhM1zXF1dcZXhc+lq9WrYsEF/2P7sMxnBk9NcuwYffaS3J0/WBXdE5suM9hGkjQS4e1d3Jl66pEe8bdwIHh5GZyUSU0ovr/Hjj+DurpdlkM6InG/gwIGULFmSbdu2UbJkSfbt28ft27cZMmQIn8rwtmxn3DgIDITHH9fTV4VIq65d9YXvgwd1Ma7PPzc6IyGEyL0MLcri4uJC9erV2bZtm/U+i8XCtm3bqFOnTpL4p556iiNHjnDw4EHrrXXr1jRq1IiDBw/mylE1me3uXXjrLb09ciSUL29sPiL9vf++nlJYs6Y+aRPGkPYxc4SHQ+vWcOwYFCkCW7aAr6/RWYnkTJ8ePw196VKoVcvojERm+PPPPxk/fjx+fn44ODjg4ODAs88+y+TJk3knRy3gnPMdOBA/xfnLL/VoMyHSytEx/vdozhw4dcrYfIQQIjczfELX4MGDCQgIoEaNGtSqVYsZM2YQGhpK9+7dAXjjjTcoUqQIkydPxs3NjQoVKtg8/7HYuU6J7xcZY+hQPYLtqaekomZO9PffsGCB3p45ExwMrwOfu0n7mLFiYqBTJ/j9d/Dx0Z2JxYoZnZVIzurV8WtlffoptG1rbD4i85jNZrxih8r7+flx5coVypYtS/HixTl58mSq9zN58mTWrFnDiRMncHd3p27dunzyySeULVs2o1IXCURHQ8+euuBVp056nVohHlbjxrow16ZNeqTr2rVGZySEELmT4R2KHTt25ObNm4wZM4Zr165RpUoVtmzZYi1EcOHCBRykVyNL2LYN/vc/PTrk66+lEEtOE1d1USno0kWvIyeMJe1jxlEK3n4b1q/XbdmGDSD9rlnT3r3w+uv6PRswAN591+iMRGaqUKEChw4domTJktSuXZspU6bg4uLCvHnzKJWGimE7d+5kwIAB1KxZk5iYGN577z1eeOEFjh07JhV4M8GUKXD4MOTLpy9YCvGopk7VFwLXrYOdO6FBA6MzEkKI3Mekclnd7uDgYHx8fAgKCsLb29vodLKN0FCoWBHOn9cf6GbNMjqjDBQRAa++itli4VWliHZ0ZPXq1bi5uRmdWYb6/nt47TW9dtzJk3p9o9xC2oV4ueVYTJ4M772nL5CsWgWvvGJ0RtlEJreP589D7dpw86YejbJunRTLMYKR7cLWrVsJDQ3llVde4cyZM7z00kucOnWKfPny8f333/P8888/1H5v3rxJ/vz52blzJ88991yqnpNb2sf0dvw4VKmii/ktXQqdOxudUQaJbR8BIpYu5dUuXQByxTmkUfr101Wfa9TQF5+MuMZqdLsQERHB+fPnKVmyZK78PVu4cCGDBg3i3r176bK/wMBASpYsyT///EOVKlUM309qjBs3jjlz5nDjxg3Wrl3Lyy+/nKGvl9G6devGvXv3WLduHQANGzakSpUqzJgxw9C8HkVm/j6kl9S2LXJaLlJlzBj9wa5oUf1BPEdzc4NNm3AE1hmdSyYJC4Phw/X2yJG5qzNR5D5LlujORNAjZaQzMQ0ysX28exdattSdiVWrSuXt3KpZs2bW7dKlS3PixAnu3LlD3rx5rZWeH0ZQUBAAvnYWTY2MjCQyMtL6fXBw8EO/Xm5lNuupzlFR+qJAp05GZ5SBYttHADdgU+y2yDgffqg7qQ8cgGXL9AwbkX1cu3aNiRMnsmnTJi5fvkz+/PmpUqUKgwYNonHjxkanl2qJO8AAihYtytWrV/HL4DLkx48f58MPP2Tt2rU888wz5M2bN0NfTzycxL8PO3bsoFGjRty9e9e6RFV2JXPlxAPt3w9xFwTmzpWqvznRp5/ChQt6/bihQ43ORoiM8/PP0KOH3h42TE97FllPVJQe6HPihL7AsXEjeHoanZUwQlBQEHfu3LG5z9fXl7t37z50B5/FYmHQoEHUq1fP7hqzkydPxsfHx3qT4lZp9+WX8Oef+twxrqiSEOklf34YNUpvjxqlC62J7CEwMJDq1avz66+/MnXqVI4cOcKWLVto1KgRAwYMMDq9R+bo6EjBggVxyuAroWfPngWgTZs2FCxYENdk1iSLiorK0BzEg2XW74MRpENR2BUVFb+Idpcu0KKF0RmJ9HbxInz8sd6eOhXc3Y3NR4iMcvCg7qSKidHT++N+70XWohT07g3bt+tOiE2boHBho7MSRnnttddYvnx5kvtXrFjBa6+99lD7HDBgAEePHk12vwmNGjWKoKAg6+3ixYsP9Xq5VWBgfGfPlCl6losQ6W3QIP27dfGirM+ZnfTv3x+TycS+fft49dVXefLJJ3n66acZPHgwe/bsscZNnz6dihUrkidPHooWLUr//v0JCQmxu+8ffviBmjVr4ubmhp+fH20TVHIzmUw2IwlBFzFcuHBhsvsym8307NmTkiVL4u7uTtmyZZmZ4Bdt3LhxLFq0iPXr12MymTCZTOzYsYPAwEBMJhMHDx60xu7cuZNatWrh6upKoUKFGDlyJDExMdbHGzZsyDvvvMPw4cPx9fWlYMGCjBs3LsWfc9y4cbSKrXDl4OBgHbXfrVs3Xn75ZSZOnEjhwoWtxceOHDnC888/j7u7O/ny5aNPnz42xzLueZMmTaJAgQI89thjjB8/npiYGIYNG4avry+PP/44C+IqeKbAYrEwZcoUSpcujaurK8WKFWPixInWxy9evEiHDh147LHH8PX1pU2bNgQGBtrd54PYe8+XLFlCjRo18PLyomDBgnTu3JkbN25YH9+xYwcmk4lNmzZRqVIl3NzceOaZZzh69Kg15vbt23Tq1IkiRYrg4eFBxYoVWbZsWap/7oS/D4GBgTRq1AjAOtuiW7duLF68mHz58tnMjAB4+eWX6dq16yMdn4wkHYrCrkmT4MgR8POLH6WY44WGQp48qDx58PfwIE+ePISGhhqdVYYZOVJf0a1fH9q3NzobITLGf//p6bP370PDhrBwoVQxfyiZ0D5OmACLF4OjI6xcCZUqpevuRTazd+9e64l3Qg0bNmTv3r1p3t9bb73Fxo0b2b59O48/YH0PV1dXvL29bW4idSwW6NVLNxnPPQd9+hidUSaIbR/Jk4fQGzfIkydPjj+HzArc3fXnFdBfE/QTiNDQlG8REamPTTz0M7mYNLhz5w5btmxhwIAByRbFSjgF1MHBgc8//5x///2XRYsW8euvvzI8bp2mZGzatIm2bdvSsmVL/vnnH7Zt20atWrXSlF9CFouFxx9/nJUrV3Ls2DHGjBnDe++9x4oVKwAYOnQoHTp0oHnz5ly9epWrV69St27dJPu5fPkyLVu2pGbNmhw6dIg5c+bwzTff8NFHH9nELVq0iDx58rB3716mTJnC+PHj+fnnn5PNbejQodbOvbjXjrNt2zZOnjzJzz//zMaNGwkNDaVZs2bkzZuX/fv3s3LlSn755Rfeeustm33++uuvXLlyhd9++43p06czduxYXnrpJfLmzcvevXvp27cvb775JpcuXUrxmI0aNYqPP/6Y0aNHc+zYMb777jtrQcno6GiaNWuGl5cXu3btYvfu3Xh6etK8efOHHkn5oPc8OjqaCRMmcOjQIdatW0dgYCDdunVLsp9hw4Yxbdo09u/fj7+/P61atSI6OhrQ6wlWr16dTZs2cfToUfr06UPXrl3Zt29fqn7uhIoWLcrq1asBOHnyJFevXmXmzJm0b98es9nMhg0brLE3btxg06ZN9IibXpUVqVwmKChIASooKMjoVLK8/fuVcnRUCpRavtzobDJRSIj+oUF5gAJUSEiI0VlliD/+0D+qyaTUX38ZnY1xpF2IlxOPxZ07SpUvr3/XK1RQ6u5dozPKxjK4fVyyxLp7NXduuu1WPCIj2wUPDw91+PDhJPcfPnxYubu7p3o/FotFDRgwQBUuXFidOnXqoXLJie1jRvniC/137O6u1EMe7uwnQfsYcv26IoefQ2YlZrNS1avrw9+vX+a+ttHtQnh4uDp27JgKDw9P+mDcP9Tkbi1b2sZ6eKQc26CBbayfX9KYNNi7d68C1Jo1a9L2wyqlVq5cqfLly2f9fsGCBcrHx8f6fZ06dVSXLl1SfD6g1q5da3Ofj4+PWrBggVJKqfPnzytA/fPPPynuY8CAAerVV1+1fh8QEKDatGljE5N4P++9954qW7asslgs1pjZs2crT09PZTablVJKNWjQQD377LM2+6lZs6YaMWJEirmsXbtWJe7SCQgIUAUKFFCRkZHW++bNm6fy5s1r0x5t2rRJOTg4qGvXrlmfV7x4cWs+SilVtmxZVb9+fev3MTExKk+ePGrZsmXJ5hMcHKxcXV3V/Pnzk318yZIlSY5DZGSkcnd3V1u3brXmkfB4NmjQQA0cODDFY/Cg9zyx/fv3K0Ddv39fKaXU9u3bFaCWJ+jwuH37tnJ3d1fff/99ivt58cUX1ZAhQ5RSD/65E/8+xL3m3UQfSvr166datGhh/X7atGmqVKlSNscrs9htWxKQ8RkiWRER8MYbejHtjh31TeQsFgu8847e7tEDqlUzNh8hMkJkpC66cuyYnja7eTNk87WPc6ydO+PXtxw+PJeMaBIPVKtWLebNm5fk/q+++orq1aunej8DBgzg22+/5bvvvsPLy4tr165x7do1wmXRtXR36lR8obepU6FMGWPzETmfgwNMm6a3583TlcVF1qWUSnXsL7/8QuPGjSlSpAheXl507dqV27dvExYWlmz8wYMH072gy+zZs6levTr+/v54enoyb948Lly4kKZ9HD9+nDp16tgUE6tXrx4hISE2o/0qJZqWUahQIZvpualVsWJFXFxcbF6/cuXKNiNC69Wrh8Vi4eTJk9b7nn76aRwSTOEpUKAAFStWtH7v6OhIvnz5Uszp+PHjREZGpvgeHDp0iDNnzuDl5YWnpyeenp74+voSERFhXQ8yrR70nv/111+0atWKYsWK4eXlRYMGDQCSvId16tSxbvv6+lK2bFmOxzYmZrOZCRMmULFiRXx9ffH09GTr1q3WfTzo506t3r1789NPP3H58mVAVzHv1q3bIxWhy2g5b1VIkS5Gj9b/jAsUgNmzjc5GZIQlS3RVPC8vSLCshRA5hlK6g2rHDv17vnmzrOGVVZ08CW3bQnS0Xnph8mSjMxJZxUcffUSTJk04dOiQ9UR927Zt7N+/n59++inV+5kzZw6gp0ontGDBgmSnPomHExOjL0iHh0OTJtCvn9EZidyiQQNo0wbWr9cd2j/8YHRGWYC9tQYdHW2/t9dplXiNmEdc765MmTKYTCZOnDhhNy4wMJCXXnqJfv36MXHiRHx9ffn999/p2bMnUVFReHh4JHmO+wMWgzeZTEk6NOOmtSZn+fLlDB06lGnTplGnTh28vLyYOnXqQy25kRrOzs5J8rVYLGneT3JTyR/29dOS04OOf0hICNWrV2fp0qVJHvP3909jtg9+zbip3s2aNWPp0qX4+/tz4cIFmjVrlqYp1lOnTmXmzJnMmDHDuqbnoEGDrPt40M+dWlWrVqVy5cosXryYF154gX///ZdNmzaly74zioxQFEn8/nv8Vb758yFfPmPzEenv/n29diLozuNklncQItv74AP47jtwcoJVq6ByZaMzEsm5eVOvb3n3LtSpA4sWyfqWIl69evX4888/KVq0KCtWrOCHH36gdOnSHD58mPr166d6P0qpZG/SmZi+PvkE9u4FHx/43//kb1lkrk8+0f/zN26EX381OpssIHZNz2Rvbm6pj03cWZJcTBr4+vrSrFkzZs+enewao/fu3QP0yDKLxcK0adN45plnePLJJ7ly5YrdfVeqVIlt27al+Li/v7/NWoOnT59OcbQjwO7du6lbty79+/enatWqlC5dOslIOhcXF8xms928ypUrx59//mnTmbl79268vLweuJ5veihXrhyHDh2yOd67d+/GwcHBWrQlPZQpUwZ3d/cU34Nq1apx+vRp8ufPT+nSpW1uPj4+D/Wa9t7zEydOcPv2bT7++GPq16/PU089leLoyoTFgO7evcupU6coV64coI9VmzZteP3116lcuTKlSpXi1KlTqf65E4sbPZrc702vXr1YuHAhCxYsoEmTJhTN4qMh5N+8sBESAgEBemRP9+4QWzhK5DCTJ8O1a1C6dPy0ZyFyknnz4hdpnzcPXnjB2HxE8sLDoXVrOHcOSpXSI0uk0rxIrEqVKixdupR///2XAwcO8L///Y8yMo82y/nnH4grSPrFFzIiXGS+smWhb1+9PWSIXt5HZE2zZ8/GbDZTq1YtVq9ezenTpzl+/Diff/65depp6dKliY6O5osvvuDcuXMsWbKEr776yu5+x44dy7Jlyxg7dizHjx/nyJEjfPLJJ9bHn3/+eWbNmsU///zDgQMH6Nu3b5IReAmVKVOGAwcOsHXrVk6dOsXo0aPZv3+/TUyJEiU4fPgwJ0+e5NatW8mOeOzfvz8XL17k7bff5sSJE6xfv56xY8cyePBgmynGGaVLly64ubkREBDA0aNH2b59O2+//TZdu3ZNtnDIw3Jzc2PEiBEMHz6cxYsXc/bsWfbs2cM333xjzcPPz482bdqwa9cuzp8/z44dO3jnnXfsFnqxx957XqxYMVxcXKy/Qxs2bGDChAnJ7mf8+PFs27aNo0eP0q1bN/z8/Hj55ZcB/Xvw888/88cff3D8+HHefPNNrl+/nuqfO7HixYtjMpnYuHEjN2/etKm23blzZy5dusT8+fOzdjGWWNKhKGwMH64/2BUtCp99ZnQ2IiOcOxc/AnXaNHB1NTYfIdLb5s3Qv7/eHjtWXxwRWY/FoqdG7tkDefPq9+0hZ7sIIQwWt/Z2TIxet/b1143OSORWY8eCtzccPKiX9xFZU6lSpfj7779p1KgRQ4YMoUKFCjRt2pRt27ZZl6ioXLky06dP55NPPqFChQosXbqUyQ9YE6Vhw4asXLmSDRs2UKVKFZ5//nmbSrzTpk2jaNGi1K9fn86dOzN06NBkp07HefPNN3nllVfo2LEjtWvX5vbt2/SPO8mM1bt3b8qWLUuNGjXw9/dn9+7dSfZTpEgRNm/ezL59+6hcuTJ9+/alZ8+efPDBB2k5bA/Nw8ODrVu3cufOHWrWrEm7du1o3Lgxs2bNSvfXGj16NEOGDGHMmDGUK1eOjh07WkcFenh48Ntvv1GsWDFeeeUVypUrR8+ePYmIiMDb2/uhXs/ee+7v78/ChQtZuXIl5cuX5+OPP+bTTz9Ndj8ff/wxAwcOpHr16ly7do0ffvjBOpLwgw8+oFq1ajRr1oyGDRtSsGBBa2djan7uxIoUKcKHH37IyJEjKVCggE21bR8fH1599VU8PT2TvEZWZFJpWRU1BwgODsbHx4egoKCH/qXNqX7+OX4Uz88/67VvcqXwcGjRArPFQguliHJ05Mcff0y3tRGM9uqrsGYNNG0KW7dCFl7jNdNIuxAvux+LAwegYUMIDYVu3fSUO/kdT0fp2D4OH64LNjg76/85sWtkiywou7cL6UWOQ8ri/p7z54ejR3PpxYHY9hEgfM0aWrzyCkCOOofMLqZMgREjoEgRXSTITn/RIzO6XYiIiOD8+fOULFkSt8TTmIUQD7Rjxw4aNWrE3bt3eSyLVG5s3LgxTz/9NJ9//rlhOaS2bZGiLALQ6/AGBOjtAQNycWci6Pl2O3bgCKR+uffsYft23Zno6KhHoEpHi8hJTp3Sn+VCQ3WH+bx58jue7tKpfZw7V3c+gO70lc5EIbKvX36BuAEf8+fn0s5EsLaPAO7oD6nCGO+8A19+Cf/9B9On6zWVhRAiq7t79y47duxgx44dfPnll0ankyoy5VlYp51dvQrlyukFjUXOExMDgwbp7X794OmnDU1HiHR15YoeYX3rFlSvDqtX65FvIuvZskVfuAL48EOZGilEdnb5MnTurNfe7t1br4kqhNHc3ODjj/X2xx/rdcOFECKrq1q1Kt26deOTTz5J12I5GUlGKAqmTNFTX93dYcWKNBfqEtnE11/D4cN6rbK4RdOFyAnu3YPmzfVIhNKl9Vp8Xl5GZyWSs307tG8PZrMeFT96tNEZCSEeVkwMvPaartReuTLMnGl0RkLE69hRz8bZt0+vqzh3rtEZCSGyooYNG5JVVgEMDAw0OoU0kw7FXO733+OnAXzxBVSoYGw+WUJoKJQogVKKEkCYyURgYCB5snFP69278e/zhx9CvnzG5iNEeomrEnzkCBQsCD/9pNfwEhnkIdvHixdh2DD4/nv9faNGMiVdpOyV2LXnUmPNmjUZmImw5/339XmklxesXCkV2uPaR4DQf/+lROxUkOx+DpldmUx6uvOzz+qL6m+/LZ9zhBAivUmHYi52+zZ06qRHinTuDNmgKnnmuXULE3ALCDM6l3Qwfrx+v8uX19OdhcgJYmJ0G7Zrl67ouGULlCxpdFa5QBrax/Bwvbba5Ml628EB+vTRS2vEFs4TIgkfHx+jUxAPsGGDnuECeh3UMmWMzSfLuHUrweYtO4EiM9Srp4sRrl6tL2r9+KPRGQkhRM4iHYq5lFK6AuqlS/Dkk/DVVzJSJKc6cQJmzdLbM2aAk/zVi1QIvXkTx4iIJPc7urjglqACWuiNGynuw8HJCXdf34eKDbt1C2WxJBtrcnDAPZ8f/frB+vXg43yLlYsslC4EoTeSxnr4+Vm/D79zB0tMTIp55EkwvDEtsRH37mGOikqXWA8/P0wOeonjyOBgYpJ5Hx4m1t3XF4fYBiAqJITosJS7A1OMDQ0l8Tibi4FRRJnzEBwM926EcP9OGCEheir6N9/AxUtgAp6vDVM+f4zqtXRPYnRYGFEhISnm4OrtjVNsVbm0xMZERBAZHJxirIunJ86xJT/TEmuOiiLi3r0UY509PHDx9ExzrCUmhvA7d9Il1snNDdfYKqPKYiHMTodGWmIj7PxeZYQFCxZk6uuJtAkMjC/k98470K6doekI8f/27ju+yXL9H/gnTXdLF6WTMmUoS2QJCqhUEBREKyJ6RBCQo6AgIOucUlCRLUN6UPmyHIhwGP4EBKFSRPYWKSAgLRztoEJbkk6S+/fH3SYtNGla2jwZn/frlVefJHeeXH0aLp5czz3Mmj1bFsB37JCjGHr2VDqimmErQzaJyDFYnFOEk8nOzhYARHZ2ttKhKGr+fCEAITw8hDh1SulobIxGIw8OILwBAUBoNBqlo6qy3r3lr9O3r9KR2C7mBSPDsSj+N3Dn7UidOmXaa0y0E4A46e9fpu11lcpk27Pe3mXaXlOrTba96OEh4uLkXRcXIS64ephse02tLrPfs97eJtteV6nKtD3p72+yreaO/z6P1Kljsq24o+2ByEizbTXp6Ya2+xo3Ntv2elKSuHZNiKNHhdjWoKXZtuNj9okRI4QYOlSIFbXbm207ossWMWCAEM89J8SSoO7ltinJj23xmeHhCehjdr8nFy40/G6JAwaYbXskLs54HIYNM9v2wLvvGo/vu++abbtv2DDj363kg2TiljhggPHzsHCh2bZ7+vQxfs5Wrzbftnt3Q9uLW7aYb9u+vfHfxb595uNt2dLQ9npSkvnj0Lixoa0mPd1s213h4YI5kv9XCCFEfr4Q7YvTR8eOQhQUKB2RDSl1/qhJTxeA/Z9DOoqxY+WfplUrIW7frt59K50Xbt++LZKSkkRmZqYi709EjikzM1MkJSWJ2xUkTfZVckK//AJMniy3Fy2SE2mTY9q+XQ7vcHMDFixQOhqi6qHTyblAASA+HnAZC8B0R0KH9vTTwJErcntpBW3/uxFIKd6+v4K2+w8AScXbrSto6wI5b6WfH+CXAcB0hz8ii7Rt2xYqC4dNnDhxooajoRJCyHnojh2TC7ytX8+pC8g+xMYCq1fL+ZZXrwaGDVM6ouqjVqsREBCAjOJRIN7e3hbnTyKiOwkhkJubi4yMDAQEBECtVpttrxJCCCvFZhNycnLg7++P7Oxs+BUP9XEmKSlAhw5yRb6XXgLWruVQ57totUDx8DIfyDnCNBqN3U2oXVgItG4NXLgATJgAzJundES2y9nzQmklx+KvS5fgV85SyUoPed65E3h1sAtyEYx//xv44IOKh0dbc8izEHKob2qqLHyWdFdxDwwxbOdnZeF2QSH0ekCvl+2KiuStsBAQXsEouu2CvDxAeyMH2ux85OUBubnA1avyolBuXvFxQjDUaheEhwP+njnw886Hjw/g7S1vXl6Ap2fxz8AgeHq7ws0NUBVpoNbnQq2W8xoWj5o2xKjyDoJK7QpXV0Ct08ANuXB1BTx0Wrw4upH8/SHzY9bfN+AfFAig4qHUngEBULtzyLO9DXnW5ucjtH59q+XIGSVXDCwQFxdXg5GU5ez/VyxcCIwbJ88bt24F+vRROiIbU+r8UZueDt/QUAD2eQ7piD7+GBg/HggPB37/3fCnume2kBeEEEhLS0OWmf93iIgqIyAgAGFhYRVeoGBB0YlotXJy4tOngbZt5UIGPL8ph4MUFEtO/ENC5IkT57g3zZnzwp1s+VgcOgQ88YRc3OP11+WqjUpdELl+XfZ0OHMGuHhRzimWkiJ/mql7VZuwMKB3b/mFPjoaKFXjrVkOkh+pcmw5L1iTMx+HrVuBfv3kBYcFC+T5Bd2BBUWbVlAgFyf84w8gLg6YPr169mtLeUGn06GoqEjRGIjI/rm5uVXYM7EEhzw7Cb1eTqB9+rQsMG3ZwmKiSS4uQPv20Ov1aAugwMUFLiXdd+zE9evGIaEzZ7KYSPbvwgXgmWdkMbFPH+suJHXrFrB3r7ydPi2LiGlp5l8THCyHAqpUZW8lvQFLegaW/HR3l1MTuLkZtz09ZZ4u6XHo4wMEBcmiaps2xl6FVuUA+ZHsT1ZWFv773//i8uXLeO+99xAUFIQTJ04gNDQUkZGRSofn8H79FRg0SBYTR4wA3n1X6YhsVHF+BGSv+/Yl28yRNsHDQy7Q8uKLctTOG28AERFKR1W91Gq1xUUAIqLqwIKik/jwQ2DjRvkldfNmoF49pSOyYV5ewNGjcAHwi9KxVFFsLJCdLXuiDh2qdDRE9yYtDXjqKeDvv4GOHeW8XW5uNfd+hYWyN+Tu3UBCAnD4sByWfKdGjYBWrYD77wcaNgTq1wcaNJD51cur5uJTlAPkR7Ivv/76K6Kjo+Hv74/k5GSMGDECQUFB2LRpE65evYovvvhC6RAdWno60Lev7Hn9xBNy3lpOlWNCcX4EAC8AR4u3yXa88ALQpQtw4IA8V16xQumIiIjsGwuKTmDTJtm1H5C9erp0UTYeqlmnTwPLl8vtxYtlDygie6XVyi+zycnAfffJYXc10bv69m0gMRFYt05efLlzGqJGjYAePeQctK1aAS1aAOVMMUlE1WzcuHEYMmQI5s6di1ql/tH16dMHL7/8soKROb68PKB/fzl3a5MmwIYNNXsxh6imqVRyyH7nzsCqVcA773BxSiKie8GCooM7fRp49VW5PXasnHeMHJcQ8u+s1wMDBgBduyodEVHV6XTAK6/IFUWDg+WK5XXqVM++Cwpkz8c//pAXXdavB0qvGRMSIguIJbcGDarnfYmoco4ePYrPPvvsrscjIyORVtHcA1RlQgDDh8ve2oGB8mJOqXWziOzWww/LYc/r18tFC3/8kb1uiYiqigVFB1ZYCLz8slwZ9MknucqvxXJzgQcegF4IPCAE8l1ckJSUBO/ilT5t2aZNspeVpyf/3mT/JkwAvvtOznv03Xeyh2JVaLVymN6PP8rVl1NTgZs3725Xu7YcDjVoEPDoo+zdWy47zo9knzw8PJBTzkrcv//+O+pU1xUGust//wusXQu4uspe202bKh2RHSjOjwCQe+wYHiieQ5E50vbMni3nk9+9G9ixQy5yRkRElWcTswTHx8ejQYMG8PT0RKdOnXDkyBGTbZcvX46uXbsiMDAQgYGBiI6ONtvemc2dCyQlyZ4233wjTwrJAkIAKSlwuXoV165dQ0pKCuxhMfT8fFmAAYD33pPzuZH9c9b8+MknwKJFcvuLL6o2VUNBgdxP48bApElyPsSkJGMx0d1dznc4eLDs/ZiaKqeF6N6dxUST7DQ/kv3q168f3n//fcPKpSqVClevXsWkSZMQExOjcHSOKTsbGDNGbk+dCjz+uLLx2I3i/IiUFAi9HikpKcyRNqphQzncGZDnzrdvKxsPEZG9Uryg+O2332LcuHGIi4vDiRMn0KZNG/Tq1QsZpceelZKYmIhBgwZhz549OHjwIKKiotCzZ0/8+eefVo7ctv3+u1yIBQAWLpQ9b8ixffyxnGcuMlIWT8j+OWt+/P57OXQfAGbNkkOTKuP2bTk3UtOm8gtDerqcAzE+XvZG+O03ucBLfr787rdmjVz0hXODEdmeBQsWQKPRICQkBHl5eejevTvuu+8+1KpVCzNnzlQ6PIf0r3/JCyxNmgBTpigdDVHNmDpVDuNPSgJWrlQ6GiIi+6QSCl8269SpEzp06IClS5cCAPR6PaKiovD2229j8uTJFb5ep9MhMDAQS5cuxeDBgytsn5OTA39/f2RnZ8PPz++e47dFQsg5v/bsAXr2lF35OTdIJWi1gK8vAMAHQC4AjUYDn5pYCaKa/PWXLJ5otcBXX8l558hytpoXrJ0fAeWPxfHjQLducuTY8OHA559XLn8dPy7njT13Tt6PiJArOb7+uuyRSPfIDvMj3Tul8wIA7N+/H6dPn4ZGo8FDDz2E6Ohoq8dgC8ehph05IueYE0JegOnRQ+mI7Eip/KhNT4dvaCgA5khbtmSJ7I0bEgJculS1xdacIS8QEZmi6CDYwsJCHD9+HFNKXf50cXFBdHQ0Dh48aNE+cnNzUVRUhCATM0UXFBSgoKDAcL+8eXgczRdfyGKilxewbBmLic5g6lR5Hvvww3LeTLJ/1siPgG3lyKtXgWeekcXEnj2B//zH8vwlhCw+vvOOnD+2dm3Zs+att2QuJCL79sgjj+CRRx5ROgyHdvs28MYbMp+++iqLieT4/vlPOTXKpUvAnDnG0V1ERGQZRYc8Z2ZmQqfTIbT4Cl6J0NBQi1fumzRpEiIiIkxeqZ41axb8/f0Nt6ioqHuO25ZlZgLjx8vtuDg5zI8c25EjcsgmACxezAKyo7BGfgRsJ0dmZwNPPy1XXm7VCtiwwfIhyFot8Npr8otBYSHQrx9w8aLMhSwmEtmnn376CQ888EC5Fzmys7PRokUL7Nu3r1L7/Pnnn9G3b19ERERApVJhy5Yt1RStY1i8GDh9Wg4DXbBA6WiIap67u5xzHpCf+f/9T9l4iIjsjeJzKN6L2bNnY926ddi8eTM8PT3LbTNlyhRkZ2cbbteuXbNylNY1YYKcG6xVK2DcOKWjoZomhHHi9NdeAzp2VDYesh2W5EfANnJkUREwYICc2zA8HNi2DbB01NCFC0CnTsCXX8qFVObMkSs3BgbWaMhEVMMWLVqEESNGlDuE0N/fHyNHjsTHH39cqX1qtVq0adMG8fHx1RWmw0hJAaZNk9vz5gFcQJucRf/+QNeucl7lf/1L6WiIiOyLokOeg4ODoVarkZ6eXubx9PR0hIWFmX3t/PnzMXv2bOzevRutW7c22c7DwwMeHh7VEq+t++kn2VNNpZJD/7jAQBWpVMADD0AvBJoLgXwXF6hstNvf2rXAoUOAjw/w0UdKR0PVyRr5EVA+RwohhyXv2iU/x1u3ApZ2kty8Wa7QrNEAYWHAunVyhWaqQXaUH8m+nT59GnPmzDH5fM+ePTF//vxK7bN3797o3bv3vYbmcIQARo+W00106wYMHap0RHaqOD8CgMrFBQ+UbDNH2jSVSvZO7NhRThs1Zgzw0ENKR0VEZB8U7aHo7u6Odu3aISEhwfCYXq9HQkICOnfubPJ1c+fOxQcffIAdO3agffv21gjV5t28KYf7AcCbb8q59KiKvL2Bs2fhkpSE4+fO4ezZs/D29lY6qrvcugVMnCi3p06Vi0+Q43CW/DhnDvB//we4uMiCoKUn8UuWADExspj42GPAyZMsJlqFneRHsn/p6elwM3Nl1NXVFdevX6/RGAoKCpCTk1Pm5ohWrpQXc9zcgE8/5dQpVVacH3H2LLyDg3H27FnmSDvRoYNxDvLx42WRnYiIKqb4kOdx48Zh+fLlWLNmDc6dO4c333wTWq0WQ4svjw4ePLjMogRz5sxBbGwsVq5ciQYNGiAtLQ1paWnQaDRK/QqKy8gAHn9czhkWEcGeas7iww/l6s6NGxvnzSTH4uj58dtv5cIpgJy765lnKn6NXi8L6WPGyBP+N9+UvRsr6LRJRHYmMjISv/32m8nnf/31V4SHh9doDLYyx2xN+vxzYMQIuT11KnD//crGQ6SUjz4CPDyAxERZYCciooopXlAcOHAg5s+fj2nTpuHBBx/EqVOnsGPHDsNCBFevXkVqaqqh/bJly1BYWIgXXngB4eHhhltlh704ij//lL1yTp8GQkKAH34A/P2Vjopq2oULwMKFcnvxYnkCRI7HkfPj/v1y3k8AGDtWDrerSGGhXHl03jx5/6OPgPh4wFXRyTuIqCb06dMHsbGxyM/Pv+u5vLw8xMXF4RlLrkLcA1uYY7YmLVoEjBwpL868/bZxDkUiZ1S/vjwfAYD33pPzOxMRkXkqIZyrU3dOTg78/f2RnZ1d7kTf9uTKFaBHD/mzbl0gIQFo2lTpqBxAbi7QoQP0QqBD8RxhR48etZkhK0IATz0F/PijXBWXV1HvnSPlhXtljWNx6ZKcluHvv4FnnwU2bpQLqpiTnS2HOCckyALiihVy/kSyMhvPj1QzlMiR6enpeOihh6BWqzF69Gg0a9YMAHD+/HnEx8dDp9PhxIkThgsslaVSqbB582b079/f4tc40v8VH31kXIBi4kRg9mwOdb5nxfkRAHL37kWH4nk4mCPtR3Y2cN99QGamvGD51lsVv8aR8gIRUWWxX4edOn8eiI6WPRQbNZJfshs0UDoqByEEkJQEFwDnAeQCsKW6+3ffyWKiu7vsXUBkT/7+G+jTR/5s3x74+uuKi4lpabKIfvo04OsrC5A9e1onXrqDjedHchyhoaE4cOAA3nzzTUyZMsXwOVOpVOjVqxfi4+OrXEx0ZkIAsbHAzJny/owZ8j6LidWgOD8CgNDrkVSyzRxpN/z95b+JUaOAuDjglVc48ouIyBwWFO3QgQNA//7A9etyrpvdu7kgh7PIywPefVduv/eevIpKZC8KCoDnnpPzvdavD3z/vVzZ2ZyUFHnx5NIlIDRUTuvQtq114iUiZdWvXx/bt2/HzZs3cenSJQgh0KRJEwQGBlZpfxqNBpcuXTLcv3LlCk6dOoWgoCDUq1evusK2WZmZcp7E5cvl/blz5bkEERmNGCEXfrtwAZg1S/beJSKi8ik+hyJZLjUVGDIEeOQRWUxs2xbYu5fFRGcydy6QnAxERRkXsyCyB3o9MHQosG+fvNq/bVvFC6n8/jvQtassJjZoIOddZDGRyPkEBgaiQ4cO6NixY5WLiQBw7NgxtG3bFm2LE8m4cePQtm1bTHPwyQOzsuT8iA0bGouJS5eymEhUHjc341zNixbJC5tERFQ+FhTtQGEhMH8+0KwZsGaNfGzIEOCnn4A6dRQNjawoOdl4lXTBgop7dhHZkmnTgG++kfMfbtwItGhhvv3p07KYeO0a0Lw58MsvckVzIqKqeuyxxyCEuOu2evVqpUOrERqNnCuxUSPggw/k/bZtgZ075ZBOIirfM88Ajz0mR1ZMnap0NEREtosFRRum1wP/7/8BrVrJq8i3bgEdOwKHDgGrVgEBAUpHSNY0bhyQnw88/jjwwgtKR0NkuVWrjPN1ffaZXEzKnEOH5Il8Rob88vvzz0BkZI2HSURk94QAjh2T06M0bCgXXrl5E3jgAeC//5XPcQ5aIvNUKnnxXqUC1q4Fjh5VOiIiItvEORRtUFoasHKlHJaSnCwfCwkB5syRq5q6sAzsdH78Edi8WS5e8cknnDyd7Mfu3cAbb8jtqVOB11833/6nn4B+/QCtVk7vsHUrL54QEVXk8mW5yNXatXLutxKNG8tFJl56qeIFsIjI6KGHgFdfBb74Ahg/Xk4zxfNvIqKyWFC0ETodsGeP7L2zZQtw+7Z8PCBAfhmfOpWrjFmNSgXUrw+9EIgSAvkuLlApeAZRWAi8847cfvvtioeKEtmKs2eBmBiZzwYNkkPuzNm5Uy44lZ8PPPmkLKJzaL+NsbH8SOTMrlyRvQ43bCjbg8rTU16YeeUVoHdvOSccWUFxfgQAlYsL6pdsM0farQ8/BNavl/M/b9kiF5YjIiIjFhQVVFgIJCYCmzbJ/6TS043Pde4MjBwJDBgAeHsrFaGT8vYGkpPhAuC80rEAWLxY9jYICQGmT1c6GiLLpKUBffoAOTnAo4/KYc/meld//70cyl9YCPTtK78ge3hYL16ykI3lRyJnc/myLCKWDF8u4eIip5N45RVZ9PDzUy5Gp1WcHwHAG0ByyTAjsltRUbJ34syZwMSJwNNPA+7uSkdFRGQ7WFC0sqwsICEB+O47+QU6K8v4XEAA8PLLspDYurVCAZJN+esv4P335fbcueylSvZBq5VFwatXgSZN5AUTc8XBTZuAgQNlT8aYGDlkjyfsRESyx/bPPwM//CBvpYczu7gA3bvLi8/PPQeEhSkXJ5GjmjRJTkN16RLw6afGUUNERMSCYo27fVsOQ/nxRzmc7/BhudhKiZAQeRL4/PNyEQJ+iabSJk6UqzI+/LCcx4XI1ul0sofMsWNA7drA9u3ypynr1gH/+Id83UsvAV9+KVeCJiJyVn/8YSwg7tkD5OYan1Or5fnigAFyiojQUKWiJHIOtWrJi/v//Kecj/TVV4HAQKWjIiKyDfzaVgNSUmQB8ccf5YIEpXshAkDz5sBTT8kiYpcunCTb5uTlAd26Qa/XoxuAAhcX/Pzzz/Dy8rJqGPv2yQnWVSpg6VIuxkP2YcIE2QPbw0P+vO8+022//BIYMkReZBk8WC5GxXxo42wkPxI5krw8ueBDSRHx4sWyz0dEyPPG3r2B6GguVGWzivMjAOTt3IluvXoBAHOkAxg2DFiyBEhKksOf589XOiIiItvAgmI1+N//ZM/DxERZRPz997LPBwbKE8CePeVCA8VzNJOt0uuBY8fgAuAkgFwA+tLdSq1Ap5MLsADAiBFAu3ZWfXuiKlm6FFi0SG6vWSNXaTZlxQr52RYCGD5cLkjForkdsIH8SGTPhJDTQRw6JG8HDwInT8r5Y0u4usr8WVJEbN2aq8vaheL8CAD627dxrGSbOdLuuboC8+bJORQ/+QQYNQpo2FDpqIiIlMeCYiXl58uTv8OH5e3IETnPXWlqtRyi2rOnvHXowF43VDmffQacPi2L0TNnKh0NUcW2bgXGjJHbH30k50Q0Zdky4K235Pabb7IHLhE5tsxMOe3NDz8AP/0EpKbe3aZuXVk8fOopubgK50wmsi0lPYR37wYmTwa+/VbpiIiIlMeCYgWEkBNg79wpb4mJckRDaWo10LKlXJm5Z0/giSd4IkhVl5kJ/PvfcvvDD4HgYGXjIarIiROygKjXy2FBkyebbrt4MTB2rNweOxb4+GP2vCEix5OUJFdi/uEHeQFaCONzrq7Agw/Ki88lt0aNmAuJbJlKJYc6t20LrF8vz2E6d1Y6KiIiZbGgaEZ8vOzenpJS9vGwMDlFSseO8vbQQ4CPjzIxkuP517+Amzfll42RI5WOhsi8a9eAZ56RiwY8+aTsfWjqS/H8+cB778ntiROB2bP5BZqIHM+mTXLRlNIjXVu3Bvr0AXr1kueO3t7KxUdEVdOmDTB0qJzzefx4YP9+pSMiIlIWC4omlP7i6+4OdO0qTwJ79QJateKXYKoZx44By5fL7U8+4VB5sm05OXI+odRU2Ut7wwbAza38tjNnGnvexsbKlRKZR4nI0ezeDQwaJIuJTzwht596Sg5pJiL798EHwLp1cgqsjRvl6DQiImfFgmI54uONxcTYWGDSJPZApJqn1wOjR8thUf/4B/Doo0pHRGRaUZHsgXPmjOy1vW2b6ake3n8fiIszbsfGWi9OIiJrOXQI6N9fLrASEyPnWOOFQSLHEhEhvyfOmCG/I3bvrnRERETKYUHxDitWyKIOAEydKr/8khMKDoYQAsEAcq3UjeqLL+Q8S76+wNy5VnlLoioRQq5w+OOPctje1q1AvXrlt50501hMnD1bnnyTnVMgPxLZujNn5JBmrVZO//D11ywmOqVSE18HcxJsh/Xee8DnnwN//GEcWURE5IxYUCxl7VpgxAi5/e67ckEMckI+PsD161ABSKmwcfXIzjYWWuLigPBwK70xURXMnStPoFUq4JtvgHbtym83Z45xmPOcOXLeRLJzCuRHIlt3+bIc9njzplxgZdMmwMND6ajI6orzIwD4ALhevE2Ox8dHDn0ePpydAIjIubkoHYCt2LgRGDxY9rx5801gwQLO70XWM306kJEBNG8OvPOO0tEQmbZ+vXEV50WLgH79ym+3YIGx3cyZLCYSkWP66y/ZIzEtTc6xvX27HGlARI5tyBC52FJ2ttKREBEphwVFyIUwBg0CdDq5ctfSpSwmkvX89ptcgAUAliyRiwAR2aIDB+SFF0AWvk0VvxcvBiZMkNszZsjpI4iIHNGwYcCVK0DjxsDOnUBgoNIREZE1qNVyEU8iImfm9EOehQDeflsuMNC/vxzG58Iyq3PLywN694ZOr0dvIVCoVuOHH36Al5dXtb+VELIoo9MBzz8vezkQ2aLLl4FnnwUKCmSvxI8/Lr9dfDwwdqzcjo0Fpk2zWohkDVbMj0S2budOYMcOubr9tm2crsTpFedHAMjbtAm9n38eAJgjHdiTT8q5U7dvVzoSIiJlOH1Bce1auSqfj4/8IswJtAl6PbB3L9QA9gPIBaDX66tl10LIgkxurrzt2AHs2QN4esohokS26O+/5QlzZibQvr3Mm+XlysWLjcXEKVNk70RyMDWYH4nsiU4nF2YA5CJVzZopGw/ZgOL8CAD627ext2SbOdKhrV0LBAQoHQURkTKctqCovX4d+X/nY+J7tQGo8d4YDfxdc5Gf5Q7PUv8raDMyTO7DxdUVXkFBVWqbm5kJYeIEQ+XiAu9SK8NVpm3ejRvQ375tMg6fkJAqtc3PyoKusLBa2noHB0NV3A20ICcHt/Pzq6WtV1AQXFzlR7pQo0FRbm6l2ur18uJy/t9a3Llg7Y8/yvNEzQ0NtFm5yM2VbUtuublAfj6guR0Abb47cnOBAk0uCjUaw3O5uUBunnGfBfCDDp4AgMnjc1HHWwOtiY+Qh58fXD1l29v5+SjIyTH5u7n7+sLN27vSbXWFhcjPyjLZ1s3bG+7FE0NVpq3+9m3k3bhRLW1dPT3h4ecHABB6PXIzM6ulbb6Zz5WzKygAnnsO+P13uZLz99/LCzB3mjvXuLDQpEly3kROHUFEjmrVKrmyc0CA7I1NRM6J5zpE5NSEk8nOzhYARDYgYjFDAEI0xGWRBw8hAHGkTp0y7TWyU1m5t5P+/mXaXlepTLY96+1dpu01tdpk24seHmXaXvTwMNn2mlpdpu1Zb2+Tba+rVGXanvT3N9lWc8dH40idOibbijvaHoiMNNtWk55uaLuvcWOzba8nJRnaJrZoabbtqrh94v/+T4glS4RYG9nebNu+jbaI5s2FqFdPiA9du5ts5w0IAALQCECICehjdr/dsdBw9y0MMB+DS5zw8xPi8ceF2PPaMLNtD7z7rvH4vvuu2bb7hg0z/t3i4sy2TRwwwPh5WLjQbNs9ffoYP2erV5tv27278fO7ZYv5tu3bG/9d7NtnPt6WLQ1tryclmT8OjRsb2mrS08223RUeLgCI7Oxs4ewMOTI7W+j1Qrz8sjxMfn5CnDlT/mvef994OKdNE0Kvt27MZEUazV35UaPRKB0V1bDSecGZlRyHP//MFmFh8p/CggVKR0U2o1R+1KSnF58/Mkc6OuZHInJmTttD8SrqYh7kWJX5mABPFCgckfPYvx+4pQdu3ADCs8y37dkT+LMQ0GiAublAdzNtp88AUoq351YQw+U/gPPF20UWxNymjVy1MfgCANMd3TDgBWBYP8DLC7i9BMA+021jY4H/N11u/zLcgiCIrGzaNDmUx9UV2LgRaNmy7PNCyDYffijvf/gh8K9/WT9OIiJrWrJErurcqJEc7kxERETkjFRCCKF0EPHx8Zg3bx7S0tLQpk0bfPLJJ+jYsaPJ9hs2bEBsbCySk5PRpEkTzJkzB3369LHovXJycuDv74++vdPx/Q8h6NqlENs3ZRm6q6vdOeS5vLb5WVm4XVCI/Hzg1i0gJwfIzpa3mzeBHF0IbtyQRcIbf2XhxvVCZGbC8FheqRGluQhGyQLj7siBK0wPNzXV1stTDrv08pI/PT0Bd/8g+NRyhY8P4OOmgY97Lry9jW28vGC4XyskCL5+rvD2BjxUGrirco3PCy1qNW4kjwHkHGEajQY+Pj4VDqX2DAiAuniZ5qJcOeTZlNLDmCvTlkOeq3/IszY/H6H16yM7Oxt+xa+xFdbMj4AxR8bHZ2PUKHksVqwAXn+9bDsh5NDmefPk/XnzjCs7kwPTauXVFdydH8lxleQFW8yRlVXZnFpayXHw9MxGfr4fNmwAXnihhgMm+1EqP2rT0+EbGgqAOdLROVJ+JCKqLMULit9++y0GDx6MTz/9FJ06dcKiRYuwYcMGXLhwASGlClolDhw4gG7dumHWrFl45plnsHbtWsyZMwcnTpxAyzu7z5SjJOkD2XBx8cOJE7L3mbPQ6WQR8O+/ZSHQUAS8YbxfUijMyir7MztbroZdVWo1ULs2EBQkfwYGyrmH/PwAf3/5088PqFVL3nx9ZSHwzp9eXjW8Eje/MDsdWz0ZtHZ+BIzHQq3Ohk7nh6lT5XyIJfR64LvvgNmzgSNH5GOLF8vVyskJMD86JVvNkZVV2Zx6p9LnkF26+OGXXzh/GpXCgqJTcpT8SERUFYoXFDt16oQOHTpg6dKlAORKaFFRUXj77bcxefLku9oPHDgQWq0WW7duNTz28MMP48EHH8Snn35a4fuVPhkcOdIPFrxEcfri4cEZGfKWni4LgoWFZW8FBXKBEK227E2jMRYNs7Jkz6J7oVIZi4AlhcGgIHkLDASCg4E6deTPku3ateVr7OLEW6sFQkIgAIQIgVyVChkZGTwZdGC2ejJo7fwIlM2RL73kh6+/lgX8ggLgq69kT8QLF2RbT0859G/EiHv+VcleMD86JVvNkZVV2Zx6p9L58eBBPzz8cA0HTPalOD8CgPbKFYQ0bAgAzJEOzlHyIxFRVSg6h2JhYSGOHz+OKVOmGB5zcXFBdHQ0Dh48WO5rDh48iHHjxpV5rFevXtiyZUu57QsKClBQYJwfMad4+Kebmxy6O1yhuevKK+rpdGWHEpfcbtyQz1UnX9+yxcCSnwEBxpu/v/FnyS0gQL62RnsIKs3HB9BqoQJwXelYyGlZIz8CpnNkcLAsGL7xhryosXMn8Ndfsk1AgJw37J13DN+dyFkwP5KdqkpONZUfn38eLCbS3YrzIyB7cGuLt4mIiByVogXFzMxM6HQ6hBYPCSgRGhqK8+fPl/uatLS0ctunpaWV237WrFmYMWPGXY8XFcnFBuxJYCAQGiq/wNeuLYf+ursbb25ucg5Ab295TlNyKykelu5F6Oam9G9DROZYIz8CpnNkZiawenXZxyIjgXfflUXGWrUs+z2IiGxBVXKqqfwYF1cjIRIRERHZFYdf5XnKlClleuzk5OQgKioKU6capjlRzJ3Df0sPJS59CwqSw4aL1/ogIqo2pnLktGmyh2KJBg2AmBjmISJyHqbyY4MGysVEREREZCsULSgGBwdDrVYjPT29zOPp6ekICwsr9zVhYWGVau/h4QEPD4+7Hp80SRbviO6Snw/ExECn1yNGCBSp1di4cSM8S1dXiGqYNfIjYDpHjh/PHEnlYH4kO1WVnGoqPxKVqzg/AkD+118j5pVXAIA5koiIHJaiM+G5u7ujXbt2SEhIMDym1+uRkJCAzp07l/uazp07l2kPALt27TLZnqjSdDpg+3aod+zArp07sX37duiqexJLogowP5JNYn4kO1WVnEpUKcX5Edu3Q1dYiO3btzNHEhGRQ1N8yPO4cePw2muvoX379ujYsSMWLVoErVaLoUOHAgAGDx6MyMhIzJo1CwAwZswYdO/eHQsWLMDTTz+NdevW4dixY/j888+V/DWIiKod8yMRUfWpKKcSERERkeUULygOHDgQ169fx7Rp05CWloYHH3wQO3bsMEyaffXqVbiUWlK4S5cuWLt2Lf79739j6tSpaNKkCbZs2YKWLVsq9SsQEdUI5kcioupTUU4lIiIiIsuphBBC6SCsKScnB/7+/sjOzoYfJwij8mi1hhV7fADkAtBoNPDx8VE0LKo5zAtGPBZkFvOjU2JekHgcyKxS+VGbng7f4kI1c6RjY14gImem6ByKREREREREREREZF9YUCQiIiIiIiIiIiKLKT6HorWVjPDOyclROBKyWVqtYbNkPoCcnByu0ufASvKBk80AUS7mSDKL+dEpMUdKzI9kVqn8qL11y7DNHOnYmB+JyJk5XUHxVvF/8FFRUQpHQvYkIiJC6RDICm7dugV/f3+lw1AUcyRVFvOj83D2HMn8SBa77z7DJnOkc3D2/EhEzsnpFmXR6/X466+/UKtWLahUKqXDsVhOTg6ioqJw7do1u5rwl3FbF+OuGiEEbt26hYiIiDKrJjsj5kjrsceYAcZtbbYQN3OkxPxoXYzbuhh31TA/EpEzc7oeii4uLqhbt67SYVSZn5+fXf0nX4JxWxfjrjxeVZaYI63PHmMGGLe1KR03cyTzo1IYt3Ux7spjfiQiZ8XLKERERERERERERGQxFhSJiIiIiIiIiIjIYiwo2gkPDw/ExcXBw8ND6VAqhXFbF+MmZ2WPnyF7jBlg3NZmr3GT7bDXzxDjti7GTUREleV0i7IQERERERERERFR1bGHIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZDEWFG3ArFmz0KFDB9SqVQshISHo378/Lly4YPY1q1evhkqlKnPz9PS0UsTS9OnT74qhefPmZl+zYcMGNG/eHJ6enmjVqhW2b99upWiNGjRocFfcKpUKo0aNKre9Usf6559/Rt++fREREQGVSoUtW7aUeV4IgWnTpiE8PBxeXl6Ijo7GxYsXK9xvfHw8GjRoAE9PT3Tq1AlHjhyxWtxFRUWYNGkSWrVqBR8fH0RERGDw4MH466+/zO6zKp81chzMkdbFHFlzOZL5kaob86N1MT/yHJKIiIxYULQBe/fuxahRo3Do0CHs2rULRUVF6NmzJ7RardnX+fn5ITU11XBLSUmxUsRGLVq0KBPDL7/8YrLtgQMHMGjQIAwbNgwnT55E//790b9/f/z2229WjBg4evRomZh37doFABgwYIDJ1yhxrLVaLdq0aYP4+Phyn587dy6WLFmCTz/9FIcPH4aPjw969eqF/Px8k/v89ttvMW7cOMTFxeHEiRNo06YNevXqhYyMDKvEnZubixMnTiA2NhYnTpzApk2bcOHCBfTr16/C/Vbms0aOhTmSObI89pgjmR+pujE/Mj+Wxx7zY0VxM0cSEdkgQTYnIyNDABB79+412WbVqlXC39/fekGVIy4uTrRp08bi9i+++KJ4+umnyzzWqVMnMXLkyGqOrHLGjBkjGjduLPR6fbnP28KxBiA2b95suK/X60VYWJiYN2+e4bGsrCzh4eEhvvnmG5P76dixoxg1apThvk6nExEREWLWrFlWibs8R44cEQBESkqKyTaV/ayRY2OOtC7myJrJkcyPVBOYH62L+ZHnkEREzow9FG1QdnY2ACAoKMhsO41Gg/r16yMqKgrPPvsszp49a43wyrh48SIiIiLQqFEjvPLKK7h69arJtgcPHkR0dHSZx3r16oWDBw/WdJgmFRYW4quvvsLrr78OlUplsp0tHOvSrly5grS0tDLH09/fH506dTJ5PAsLC3H8+PEyr3FxcUF0dLSif4Ps7GyoVCoEBASYbVeZzxo5NuZI62GOVDZHMj9SZTE/Wg/zI88hiYicHQuKNkav12Ps2LF45JFH0LJlS5PtmjVrhpUrV+K7777DV199Bb1ejy5duuB///uf1WLt1KkTVq9ejR07dmDZsmW4cuUKunbtilu3bpXbPi0tDaGhoWUeCw0NRVpamjXCLdeWLVuQlZWFIUOGmGxjC8f6TiXHrDLHMzMzEzqdzqb+Bvn5+Zg0aRIGDRoEPz8/k+0q+1kjx8UcaV3Mkcr9DZgfqbKYH62L+ZHnkEREzs5V6QCorFGjRuG3336rcG6Pzp07o3Pnzob7Xbp0wf3334/PPvsMH3zwQU2HCQDo3bu3Ybt169bo1KkT6tevj/Xr12PYsGFWieFerVixAr1790ZERITJNrZwrB1RUVERXnzxRQghsGzZMrNtHeGzRtWDOdK6mCOVwfxIVcH8aF3Mj8phjiQisg3soWhDRo8eja1bt2LPnj2oW7dupV7r5uaGtm3b4tKlSzUUXcUCAgLQtGlTkzGEhYUhPT29zGPp6ekICwuzRnh3SUlJwe7duzF8+PBKvc4WjnXJMavM8QwODoZarbaJv0HJiWBKSgp27dpl9spyeSr6rJFjYo60LubIil9TE5gfqSqYH62L+bHi19QU5kgiItvBgqINEEJg9OjR2Lx5M3766Sc0bNiw0vvQ6XQ4c+YMwsPDayBCy2g0Gly+fNlkDJ07d0ZCQkKZx3bt2lXmyq01rVq1CiEhIXj66acr9TpbONYNGzZEWFhYmeOZk5ODw4cPmzye7u7uaNeuXZnX6PV6JCQkWPVvUHIiePHiRezevRu1a9eu9D4q+qyRY2GOZI6sLHvNkcyPVFnMj8yPlWWv+RFgjiQisjlKrghD0ptvvin8/f1FYmKiSE1NNdxyc3MNbV599VUxefJkw/0ZM2aInTt3isuXL4vjx4+Ll156SXh6eoqzZ89aLe7x48eLxMREceXKFbF//34RHR0tgoODRUZGRrkx79+/X7i6uor58+eLc+fOibi4OOHm5ibOnDljtZhL6HQ6Ua9ePTFp0qS7nrOVY33r1i1x8uRJcfLkSQFAfPzxx+LkyZOGlexmz54tAgICxHfffSd+/fVX8eyzz4qGDRuKvLw8wz6eeOIJ8cknnxjur1u3Tnh4eIjVq1eLpKQk8cYbb4iAgACRlpZmlbgLCwtFv379RN26dcWpU6fKfN4LCgpMxl3RZ40cG3Mkc2R57DFHMj9SdWN+ZH4sjz3mx4riZo4kIrI9LCjaAADl3latWmVo0717d/Haa68Z7o8dO1bUq1dPuLu7i9DQUNGnTx9x4sQJq8Y9cOBAER4eLtzd3UVkZKQYOHCguHTpksmYhRBi/fr1omnTpsLd3V20aNFCbNu2zaoxl9i5c6cAIC5cuHDXc7ZyrPfs2VPu56IkNr1eL2JjY0VoaKjw8PAQPXr0uOv3qV+/voiLiyvz2CeffGL4fTp27CgOHTpktbivXLli8vO+Z88ek3FX9Fkjx8YcaX3MkTWTI5kfqboxP1of8yPPIYmISFIJIUQVOzcSERERERERERGRk+EcikRERERERERERGQxFhSJiIiIiIiIiIjIYiwoEhERERERERERkcVYUCQiIiIiIiIiIiKLsaBIREREREREREREFmNBkYiIiIiIiIiIiCzGgiIRERERERERERFZjAVFIiIiIiIiIiIishgLilRlycnJUKlUOHXqlMWvGTJkCPr372+2zWOPPYaxY8feU2wqlQpbtmwBYHmclrxv6f1a0/Tp06FSqaBSqbBo0aJ72tfq1asREBBgtfcjclbMkdbDHElkX5gfrYf5kYiIagoLig4sLS0Nb7/9Nho1agQPDw9ERUWhb9++SEhIUDo0q4qKikJqaipatmwJAEhMTIRKpUJWVlal95WamorevXtXc4SWadGiBVJTU/HGG2/c9dysWbOgVqsxb968anmvCRMmIDU1FXXr1q2W/RHZIuZIiTmy8pgjydExP0rMj5XH/EhE5DxYUHRQycnJaNeuHX766SfMmzcPZ86cwY4dO/D4449j1KhRSodnVWq1GmFhYXB1db3nfYWFhcHDw6Maoqo8V1dXhIWFwdvb+67nVq5ciYkTJ2LlypXV8l6+vr4ICwuDWq2ulv0R2RrmSCPmyMpjjiRHxvxoxPxYecyPRETOgwVFB/XWW29BpVLhyJEjiImJQdOmTdGiRQuMGzcOhw4dAgC8/vrreOaZZ8q8rqioCCEhIVixYgUAQK/XY+7cubjvvvvg4eGBevXqYebMmeW+p06nw7Bhw9CwYUN4eXmhWbNmWLx4cbltZ8yYgTp16sDPzw///Oc/UVhYaPJ3KSgowIQJExAZGQkfHx906tQJiYmJFh+L0sNVkpOT8fjjjwMAAgMDoVKpMGTIEENbvV6PiRMnIigoCGFhYZg+fXqZfZUerlLeVepTp05BpVIhOTkZgHFoyNatW9GsWTN4e3vjhRdeQG5uLtasWYMGDRogMDAQ77zzDnQ6ncW/U2l79+5FXl4e3n//feTk5ODAgQMWvW7nzp24//774evri6eeegqpqalVen8ie8QcacQcWT7mSHJWzI9GzI/lY34kIiIAuPfLbWRzbty4gR07dmDmzJnw8fG56/mSuU+GDx+Obt26ITU1FeHh4QCArVu3Ijc3FwMHDgQATJkyBcuXL8fChQvx6KOPIjU1FefPny/3ffV6PerWrYsNGzagdu3aOHDgAN544w2Eh4fjxRdfNLRLSEiAp6cnEhMTkZycjKFDh6J27domTzJHjx6NpKQkrFu3DhEREdi8eTOeeuopnDlzBk2aNKnUsYmKisLGjRsRExODCxcuwM/PD15eXobn16xZg3HjxuHw4cM4ePAghgwZgkceeQRPPvlkpd6ntNzcXCxZsgTr1q3DrVu38Pzzz+O5555DQEAAtm/fjj/++AMxMTF45JFHDMe9MlasWIFBgwbBzc0NgwYNwooVK9ClS5cKY5o/fz6+/PJLuLi44B//+AcmTJiAr7/+uqq/JpHdYI40jTnSGBNzJDkj5kfTmB+NMTE/EhERAECQwzl8+LAAIDZt2lRh2wceeEDMmTPHcL9v375iyJAhQgghcnJyhIeHh1i+fHm5r71y5YoAIE6ePGly/6NGjRIxMTGG+6+99poICgoSWq3W8NiyZcuEr6+v0Ol0QgghunfvLsaMGSOEECIlJUWo1Wrx559/ltlvjx49xJQpU0y+LwCxefPmcuPcs2ePACBu3rxZ5jXdu3cXjz76aJnHOnToICZNmlTufsvbz8mTJwUAceXKFSGEEKtWrRIAxKVLlwxtRo4cKby9vcWtW7cMj/Xq1UuMHDnS5O8TFxcn2rRpc9fj2dnZwsvLS5w6dcrw/r6+vmX2fafyYoqPjxehoaF3ta1fv75YuHChyX0R2SPmSOZI5kii8jE/Mj8yPxIRkaU45NkBCSEsbjt8+HCsWrUKAJCeno4ffvgBr7/+OgDg3LlzKCgoQI8ePSzeX3x8PNq1a4c6derA19cXn3/+Oa5evVqmTZs2bcrM4dK5c2doNBpcu3btrv2dOXMGOp0OTZs2ha+vr+G2d+9eXL582eK4LNW6desy98PDw5GRkXFP+/T29kbjxo0N90NDQ9GgQQP4+vqWeawq7/PNN9+gcePGaNOmDQDgwQcfRP369fHtt99WKqbq+D2J7AVzZNUxRxI5NubHqmN+JCIiZ8Mhzw6oSZMmUKlUJoeVlDZ48GBMnjwZBw8exIEDB9CwYUN07doVAMoM47DEunXrMGHCBCxYsACdO3dGrVq1MG/ePBw+fLhKvwcAaDQaqNVqHD9+/K7JnUufTFUXNze3MvdVKhX0en25bV1cZD2+9Ml3UVGRRfuszPuYs2LFCpw9e7bMZOF6vR4rV67EsGHDTL6uvPevzJcIInvGHFl1zJFEjo35seqYH4mIyNmwoOiAgoKC0KtXL8THx+Odd965aw6crKwswxw4tWvXRv/+/bFq1SocPHgQQ4cONbRr0qQJvLy8kJCQgOHDh1f4vvv370eXLl3w1ltvGR4r7wrw6dOnkZeXZzjZPHToEHx9fREVFXVX27Zt20Kn0yEjI8Nwknqv3N3dAaDKE1iXqFOnDgAgNTUVgYGBAOSE2tZy5swZHDt2DImJiQgKCjI8fuPGDTz22GM4f/48mjdvbrV4iOwFc6R5zJFEzov50TzmRyIiIiMOeXZQ8fHx0Ol06NixIzZu3IiLFy/i3LlzWLJkCTp37lym7fDhw7FmzRqcO3cOr732muFxT09PTJo0CRMnTsQXX3yBy5cv49ChQ4bV++7UpEkTHDt2DDt37sTvv/+O2NhYHD169K52hYWFGDZsGJKSkrB9+3bExcVh9OjRhqu1pTVt2hSvvPIKBg8ejE2bNuHKlSs4cuQIZs2ahW3btlXp2NSvXx8qlQpbt27F9evXodFoqrSf++67D1FRUZg+fTouXryIbdu2YcGCBVXaV1WsWLECHTt2RLdu3dCyZUvDrVu3bujQoYPh77R06dJKDTkicgbMkaYxRxI5N+ZH05gfiYiIjFhQdFCNGjXCiRMn8Pjjj2P8+PFo2bIlnnzySSQkJGDZsmVl2kZHRyM8PBy9evVCREREmediY2Mxfvx4TJs2Dffffz8GDhxocp6UkSNH4vnnn8fAgQPRqVMn/P3332WuNJfo0aMHmjRpgm7dumHgwIHo168fpk+fbvJ3WbVqFQYPHozx48ejWbNm6N+/P44ePYp69epV/sAAiIyMxIwZMzB58mSEhoZi9OjRVdqPm5sbvvnmG5w/fx6tW7fGnDlz8OGHH1ZpX5VVWFiIr776CjExMeU+HxMTgy+++AJFRUXIzMyskbmCiOwZc6RpzJFEzo350TTmRyIiIiOV4KQXTk+j0SAyMhKrVq3C888/r3Q4VI7p06djy5YtVh0OAwANGjTA2LFjMXbsWKu+L5EtYY60fcyRRMpgfrR9zI9ERFRT2EPRien1emRkZOCDDz5AQEAA+vXrp3RIZMaZM2fg6+uL//znPzX+Xh999BF8fX3vWl2RyJkwR9oX5kgi62F+tC/Mj0REVBPYQ9GJJScno2HDhqhbty5Wr17NOVJs2I0bN3Djxg0AciJvf39/h3o/IlvEHGk/mCOJrIv50X4wPxIRUU1hQZGIiIiIiIiIiIgsxiHPREREREREREREZDEWFImIiIiIiIiIiMhiLCgSERERERERERGRxVhQJCIiIiIiIiIiIouxoEhEREREREREREQWY0GRiIiIiIiIiIiILMaCIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZLH/D8BQdK+ElC/vAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -547,7 +551,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABRQAAAEiCAYAAACIil5LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADHMUlEQVR4nOzdd3gUVRfA4d8mpJMEAiFBCL0I0jsIUgRBFCmKNOkgvUgHaaLSq1QFJICKShFU+EREQu+91yAtdEhISN/5/rhkkyWFBJLMbnLe59mH2c3ZmZPZ5Wb3zr3nGjRN0xBCCCGEEEIIIYQQQohksNE7ASGEEEIIIYQQQgghhPWQDkUhhBBCCCGEEEIIIUSySYeiEEIIIYQQQgghhBAi2aRDUQghhBBCCCGEEEIIkWzSoSiEEEIIIYQQQgghhEg26VAUQgghhBBCCCGEEEIkm3QoCiGEEEIIIYQQQgghkk06FIUQQgghhBBCCCGEEMkmHYpCCCGEEEIIIYQQQohkkw5FIYQQIpPo1KkTBQoU0DsNIYQQQgghhJWTDkUhhBAinfj6+mIwGEw3R0dHihUrRt++fblz547e6SXq6dOnzJ8/n3feeYfcuXPj6upK+fLlWbhwIdHR0fHijUYjU6dOpWDBgjg6OlKmTBlWrVoVL+7AgQP07t2bihUrYmdnh8FgSPD4oaGhdO3alVKlSuHu7k7WrFkpW7Ysc+bMITIyMtV/XyGEEEIIIUTSsuidgBBCCJHZTJgwgYIFCxIWFsauXbtYuHAhmzZt4tSpUzg7O+udXjxXrlyhX79+vP322wwaNAg3Nzc2b95M79692bdvH8uXLzeL//zzz5k8eTLdu3encuXKbNiwgbZt22IwGGjdurUpbtOmTSxZsoQyZcpQqFAhLly4kODxQ0NDOX36NI0bN6ZAgQLY2NiwZ88ePvvsM/bv389PP/2Upr+/EEIIIYQQwpxB0zRN7ySEEEKIzMDX15fOnTtz8OBBKlWqZHp88ODBzJw5k59++ok2bdrEe15ISAguLi6vfPxOnTrh5+fH1atXU/S8+/fvc+fOHd544w2zx7t06cKyZcu4ePEiRYoUAeDmzZsULFiQTz/9lHnz5gGgaRq1a9fG39+fq1evYmtrC8CdO3dwc3PDycmJvn37Mn/+fFLysaRfv37MmzePgIAAvL29U/Q7CSGEEEIIIV6eTHkWQgghdFavXj0A/P396dSpE1mzZuXy5cs0btwYV1dX2rVrB8DOnTtp2bIl+fLlw8HBAR8fHz777DNCQ0Pj7XP9+vWUKlUKR0dHSpUqxW+//ZbgsadPn06NGjXIkSMHTk5OVKxYkTVr1pjF5MyZM15nIkDz5s0BOHv2rOmxDRs2EBkZSe/evU2PGQwGevXqxY0bN9i7d6/pcS8vL5ycnJJ7muKJqQf5+PHjl96HEEIIIYQQIuVkyrMQQgihs8uXLwOQI0cOAKKiomjYsCE1a9Zk+vTppmnQq1ev5unTp/Tq1YscOXJw4MAB5s6dy40bN1i9erVpf3///TcffvghJUuWZNKkSTx48IDOnTuTN2/eeMeeM2cOH3zwAe3atSMiIoKff/6Zli1b8ueff/Lee+8lmfft27cB1eEY4+jRo7i4uFCiRAmz2CpVqph+XrNmzZSeIgAiIiIICgoiNDSUQ4cOMX36dPLnz28aHSmEEEIIIYRIH9KhKIQQQqSzwMBA7t+/T1hYGLt372bChAk4OTnx/vvvs3fvXsLDw2nZsiWTJk0ye96UKVPMRvR9+umnFClShFGjRnHt2jXy5csHwPDhw/Hy8mLXrl24u7sDULt2bd555x3y589vts8LFy6Y7bNv375UqFCBmTNnJtmhGBERwezZsylYsCCVK1c2PR4QEICXl1e8BVZy584NwK1bt1JyqsysW7fObEp4pUqV+P7778mSRT7OCCGEEEIIkZ7kE7gQQgiRzurXr292P3/+/Pz444/kyZPH9FivXr3iPS9ux19ISAihoaHUqFEDTdM4evQo+fLlIyAggGPHjjFixAhTZyJAgwYNKFmyJCEhIYnu89GjR0RHR1OrVq0EV2WOq2/fvpw5c4aNGzeadeiFhobi4OAQL97R0dH085dVt25dtmzZwuPHj9m6dSvHjx+P9/sIIYQQQggh0p50KAohhBDpbP78+RQrVowsWbLg5eVF8eLFsbGJLWucJUuWBKcnX7t2jbFjx/L777/z6NEjs58FBgYC8N9//wFQtGjReM8vXrw4R44cMXvszz//5KuvvuLYsWOEh4ebHn9+hGFc06ZNY/HixXz55Zc0btzY7GdOTk5m+4kRFhZm+vnL8vLywsvLC4CPPvqIiRMn0qBBAy5evCiLsgghhBBCCJGOZFEWIYQQIp1VqVKF+vXrU6dOHUqUKGHWmQjg4OAQ77Ho6GgaNGjAxo0bGT58OOvXr2fLli34+voCYDQaU5zHzp07+eCDD3B0dGTBggVs2rSJLVu20LZt20RXW/b19WX48OH07NmT0aNHx/t57ty5uX37drznBwQEAPDaa6+lOM/EfPTRRwQHB7Nhw4ZU26cQQgghhBDixWSEohBCCGEFTp48yYULF1i+fDkdOnQwPb5lyxazuJgaiRcvXoy3j/Pnz5vdX7t2LY6OjmzevNlsmvKyZcsSzGHDhg1069aNFi1aMH/+/ARjypUrx5IlSzh79iwlS5Y0Pb5//37Tz1NLzPTpmNGZQgghhBBCiPQhIxSFEEIIK2BrawtgNvJP0zTmzJljFpc7d27KlSvH8uXLzTratmzZwpkzZ+Lt02AwEB0dbXrs6tWrrF+/Pt7xd+zYQevWrXnrrbf48ccf442gjNG0aVPs7OxYsGCBWZ6LFi0iT5481KhRI/m/9DP3799PcMTkkiVLALU4ixBCCCGEECL9yAhFIYQQwgq8/vrrFC5cmCFDhnDz5k3c3NxYu3ZtvFqKAJMmTeK9996jZs2adOnShYcPHzJ37lzeeOMNgoODTXHvvfceM2fOpFGjRrRt25a7d+8yf/58ihQpwokTJ0xx//33Hx988AEGg4GPPvqI1atXmx2vTJkylClTBoC8efMycOBApk2bRmRkJJUrV2b9+vXs3LmTH3/80dQxGrPflStXAnDo0CEAvvrqK0CNtGzfvj0AP/zwA4sWLaJZs2YUKlSIJ0+esHnzZrZs2UKTJk2oV69eapxiIYQQQgghRDJJh6IQQghhBezs7Pjjjz/o378/kyZNwtHRkebNm9O3b1/Kli1rFtuoUSNWr17N6NGjGTlyJIULF2bZsmVs2LABPz8/U1y9evVYunQpkydPZuDAgRQsWJApU6Zw9epVsw5Ff39/02jHPn36xMtt3Lhxpg5FgMmTJ5M9e3a+/fZbfH19KVq0KD/88ANt27Y1e56/vz9jxowxeyzmfu3atU0dijVr1mTPnj2sWrWKO3fukCVLFooXL87MmTPp16/fS5xNIYQQQgghxKswaIlVXRdCCCGEEEIIIYQQQojnSA1FIYQQQgghhBBCCCFEskmHohBCCCGEEEIIIYQQItmkQ1EIIYQQQgghhBBCCJFs0qEohBBCCCGEEEIIIYRINulQFEIIIYQQQgghhBBCJJt0KAohhBBCCCGEEEIIIZIti94JpDej0citW7dwdXXFYDDonY4QwgJomsaTJ0947bXXsLHJ3NdZpI0UQjxP2khF2kchxPOkfRRCZGaZrkPx1q1b+Pj46J2GEMICXb9+nbx58+qdhq6kjRRCJCazt5HSPgohEpPZ20chROaU6ToUXV1dAdXou7m56ZyNMBMSAq+9BoA3cPnWLVxcXPTNSWQKQUFB+Pj4mNqHzEzaSMsVcu8eLkWKqO1Ll3Dx9NQ5I5FZSBupSPtouaR9FHqR9lEIkZllug7FmCkqbm5u8mHQ0tjamjYNqNdIOhRFepIpbNJGWjLbsDBiWkRbV1dc5PUR6Syzt5HSPlouaR+F3jJ7+yiEyJyk0IMQQgghhBBCCCGEECLZpENRCCGEEEIIIYQQQgiRbLp2KO7YsYMmTZrw2muvYTAYWL9+/Quf4+fnR4UKFXBwcKBIkSL4+vqmeZ5CCJHepH0UQgghhBBCCGGpdO1QDAkJoWzZssyfPz9Z8f7+/rz33nvUrVuXY8eOMXDgQLp168bmzZvTOFORLhwdif7nH47NmsXGf/7B0dFR74yE0I20j+J5jtmycWzWLI7NmoVjtmx6pyOEEBZD2kchhBAi/Rk0TdP0TgJUIdvffvuNZs2aJRozfPhwNm7cyKlTp0yPtW7dmsePH/PXX38l6zhBQUG4u7sTGBgoBbWFEIDltwvp1T6C5Z8LIUT6k3ZBkfMghHietAtCiMzMqlZ53rt3L/Xr1zd7rGHDhgwcOFCfhIRVCwuDmzfh+nW4cQMCA/XOSOglNFTvDF6dtI/CmmgaXLoEe/ZAcLDe2YgXyQhtpBCpIToaHj6Ee/fg/n3174MHkDUrvPYa5M6t/nV11TtTIYQQIu1ZVYfi7du38fLyMnvMy8uLoKAgQkNDcXJyivec8PBwwsPDTfeDgoLSPE/xkiIjiV64kF27dnGmZk269eqFnZ1dqh7i8GEYMADOn1cfBIXIKF6mfQRpI61J5NOn7OnUCYAavr7YOTvrm1AKaJpqd7dvVzc/PwgI0DsrIURGkdbto6ZBv36wcCEYjS+Oz5oV8ueHt9+Gxo2hdm2QSj5CCCEyGqvqUHwZkyZN4osvvtA7DZEcERHYDhhAbaDx6tV06No1VTsUg4KgRQu4di32MWdn8PGBvHkhe3YwGFLtcMKKREZCMtY8yZCkjbQeEcHB1F69GoCQefOspkPx9Gno3BkOHjR/3N4eqlQBb+/Yx6QNtkyZuY0U1iGt28flyyFuSWMPD8iZEzw91XZwMNy6pW5Pnqj7p0+r2zffqM+b9eqpzsXmzc3bPSGEEMJaWVWHore3N3fu3DF77M6dO7i5uSU6+mbkyJEMGjTIdD8oKAgfH580zVNYpgEDVGdiwYKwbp26cpwtm3yBFaqz2d1d7yxezcu0jyBtpEg70dEwaxaMHg3h4eDgANWrq5E6tWtDtWqQxFtTWJCM0EYK8bKuXVOfIQG+/BJGjIAsSXyDCg5WI7BPnYJNm9Tt1i348091Gz4c5s6FDh3kM6gQQgjrZlUditWrV2fTpk1mj23ZsoXq1asn+hwHBwccHBzSOjVh4davB19f9cFtxQooV07nhIRIZS/TPoK0kSJtXLoEnTrB7t3qfuPGsHixqi0mhBDWwmiELl1Up3q1ai/uTAQ13bloUXVr3lxNlz5xQnUs/vILHD+u2sfff4dvv1UjHYUQQghrZKPnwYODgzl27BjHjh0DwN/fn2PHjnHt2ZzUkSNH0qFDB1N8z549uXLlCsOGDePcuXMsWLCAX3/9lc8++0yP9IWVuHsXPv1UbQ8dCjVr6puPEMkh7aOwRpoGCxZA2bKqMzFrVliyRI3Kkc5EIYS1WbgQtm5Vo6mXL39xZ2JCDAbVJo4cqWp5T5yo9rNuHZQqBRs3pn7eQgghRHrQtUPx0KFDlC9fnvLlywMwaNAgypcvz9ixYwEICAgwfXkGKFiwIBs3bmTLli2ULVuWGTNmsGTJEho2bKhL/sLyaZrqTLx3D0qXhgkT9M5IiOSR9lFYG02DPn3U7elTqFsXTp6Erl1lWp8QwvpcvKguRANMmQLFir36Pm1tVcfigQNQsiTcuQPvvw89eqh2UwghhLAmBk3TNL2TSE9BQUG4u7sTGBiIm5ub3umIuEJC1HAWwAW4GxyMi4vLK+3S11ctBmBnpxYEKFv21dMUGY+0C7HkXFiukLt3cXm2knfInTu45Mqlc0axNA0++wzmzFGdhzNnQv/+YKPrZUuRWqRdUOQ8WK7Ubh+jo6FWLdi7Vy2msmVL6rdnYWEwapSqNQuqY3H9etXpKKyHtAtCiMxMPuqLDOvqVfWFFlQRbelMFEKI1KdpasTNnDnq/pIlMHCgdCYKIazX9OmqM9HVFZYtS5v2zNFRXXz5+2+1/eefqkajEEIIYS2salEWkcE5OBC9YQNHjhxhdYUKr7RQhNGoCl4/eQJvvglDhqRemkIIoQcHNzcOjhsHQHkLGgXxxRdqOiCoemNduuibjxAi80nN9vHkSXhWXYQ5cyBfvlfNLmkNGqhOyzZtVEfm66+rUhFCCCGEpZMpzyJDmjkTBg8GFxe1ml7hwnpnJCyZtAux5FyIlJg0SU3ZA5g9GwYM0DUdkUakXVDkPGR8kZFQtSocPQpNmsCGDelXA3b8eHWBxs5OTbGuXTt9jitejbQLQojMTCYkiQzn9OnYL7gzZ0pnohBCpIVZs2Lb2ilTpDNRCGH9vv5adSZ6eMB336XvglLjxkGrVqpTs0ULuHw5/Y4thBBCvAzpUBSWIzKSqKVL2dWtG8uXLCEyMjLFu4iIgPbtITwcGjeG7t3TIE8hhNBB5NOn7OrWjV3duhGp83Kgq1fDoEFq+4svYNgwXdMRQmRyqdE+Hj4MX32lthcsAG/vVEwwGQwGNfW5cmV4+FAt0hIYmL45CCGEECkhU56F5UiFVZ5Hj1ZXl3PkUDVwcudOgzxFhiPtQiw5F5bLUlZ5PnQI3noLQkPVqMRZs9J3FI9If9IuKHIeLNerto9hYVCpkprl8vHH8MsvaZFl8gQEQJUqcOMGNGwIGzfKys+WTNoFIURmJiMURYaxb5+q5wWwaJF0JgohRGq7eRM++EB1JjZuDDNmSGdiZrBxo94ZCJG2xo1TnYm5csH8+frmkjs3/P47ODvD5s1qYRghhBDCEkmHosgQQkLUVGejEdq1g48+0jsjIYTIWJ4+VZ2JAQHwxhuwapWMmskMfv4Z2rbVOwsh0s6ePTBtmtpevBhy5tQ3H4Dy5dXob1C1as+d0zcfIYQQIiHSoSgyhKFD4dIlyJsX5s3TOxthbW7e1DsDISyb0QgdOsCRI+rL9h9/gMzsyvhOn4auXfXOQoi0ExICHTuCpql/P/hA74xide+upjyHh6vcoqL0zkgIIYQwJx2Kwupt3gwLF6rtZcsgWzZd0xFWxmiEPn30zkIIyzZuHKxdC/b28NtvULCg3hmJtBYYqFaaffoU6tTROxsh0sbIkeqCdJ48MHu23tmYMxhgyRJwd4cDB2JHUQohhBCWQjoUhVV7+BA6d1bb/fpB/fr65iOsz4IFsG2b3lkIYblWrYpd+fS776BmTX3zEWlP06BTJ7hwAfLlg++/1zsjIVLftm0wd67a/v57y7wgnTcvfPON2h43Ti04KIQQQlgK6VAUVq1PH1XPq3hxmDxZ72yEtTl/HoYN0zsLISzX0aOxU16HDVPT7kTGN3UqrF+vRqSuWQM5cuidkRCpKygo9oJ0jx7wzjv65pOU9u3VVOzISFV6IiJC74yEEEIIRToUheVwcCB61Sr2fvYZy1etwsHBIcnwn39WN1tbWLlSrYYnRHJFRakP5qGhMp1PWAcHNzf2fvYZez/7DId0KGB4/z40b67+j7z7LkycmOaHFBZg61a1CASomsSVK+ubT0ImTZpE5cqVcXV1JVeuXDRr1ozz588n+RxfX18MBoPZzdHRMZ0yFmktpe3jkCHw33+qfIOlTyU2GODbb1XH/rFj8PXXemckhBBCKFn0TkAIkyxZsG3dmuqtW78w9OZN6N1bbY8ebZlfeIRlmzhR1SRyd1fTnkuW1DsjIZKWxdGR6jNnpsuxoqKgdWv1hbtIEfjpJ1nROTO4fl297kYjdOkC3brpnVHCtm/fTp8+fahcuTJRUVGMGjWKd955hzNnzuDi4pLo89zc3Mw6Hg0GQ3qkK9JBStrHv/5SqzmDqr3t6pqGiaUSb2/1WaVVK9Wh+MEHULGi3lkJIYTI7KRDUVgdTVNT8B49gkqV4PPP9c5IWJtDh+DLL9X2/PmqGLsQItaIEWqkmouLWoTFEmuLidQVHg4ffaRGplaooEYnWmp/219//WV239fXl1y5cnH48GHeeuutRJ9nMBjw9vZO6/SEBXv0KLaMw4ABULu2vvmkxMcfq8Wxfv1VlZ84fBheMJlHCCGESFMy5VlYjqgoon/+mb2DBrHm55+JiopKMGzRIrWys6MjrFgBdnbpnKewaqGhqh5RVBS0bAlt2+qdkRDJExUWxt5Bg9g7aBBRYWFpdpxVq2DGDLW9fDmUKpVmhxIWZOBANWo7e3ZVN9HJSe+Mki8wMBAADw+PJOOCg4PJnz8/Pj4+NG3alNOnT6dHeiIdJLd9HDAAbt2CYsWss4zD/Png5QWnT6tFWoQQQgg9GTRN0/ROIj0FBQXh7u5OYGAgbulQg0qkQEgIZM0KgAtwNzg43tSlixehXDl4+hRmz1YfDIVIic8+U+8db284dUrVJJJ2IZacC8sVcvcuLl5eavvOHVxy5Ur1Yxw7BjVqqI73kSOt8wu3SDlfX7VAhcEAmzZBo0bmP7fkdsFoNPLBBx/w+PFjdu3alWjc3r17uXjxImXKlCEwMJDp06ezY8cOTp8+Td68eRN8Tnh4OOHh4ab7QUFB+Pj4WOR5yOyS0z6uX6/qwtrYwO7dUK1aOieZSjZsgGbN1O+xaxdUr653RpmbJbePQgiR1mSEorAaMYtoPH0K9epBv356ZySszb//qs5EgKVLZeVSIeJ68CB2EZZGjWLLAoiM7dgx6NVLbY8fH78z0dL16dOHU6dO8fPPPycZV716dTp06EC5cuWoXbs269atw9PTk2+//TbR50yaNAl3d3fTzcfHJ7XTF+nk/n21mjPA0KHW25kI0LSp+jxsNKqpz0+f6p2REEKIzEo6FIXVmDIF9u0DNzdVRNtG3r0iBQIDoVMntf3pp9C4sa7pCGFRoqPV9P+rV6FQIVmEJbN49AhatICwMNUmjh6td0Yp07dvX/7880+2bduW6CjDxNjZ2VG+fHkuXbqUaMzIkSMJDAw03a5fv/6qKQsdaJrqNL97F954A774Qu+MXt2cOar+88WLsauyCyGEEOlNumSEVTh6VI2cAJg7F/Ll0zUdYYX691crmBYqFFsfTgihjBsHf/+t6ub99puqoycyNqMRPvkE/P1Vu/jDD9ZzoU7TNPr27ctvv/3Gv//+S8GCBVO8j+joaE6ePEnu3LkTjXFwcMDNzc3sJqzPL7+ouqBZsqi6sBlhIZNs2WDJErU9Zw74+emZjRBCiMzKSj46iswsLCx2EY0WLdS2ECmxbp1awMfGRv37rFSnEAJVj+vrr9X2kiVQpoy++Yj08dVXql6io6NaOdaaOpH79OnDDz/8wE8//YSrqyu3b9/m9u3bhIaGmmI6dOjAyJEjTfcnTJjA33//zZUrVzhy5AiffPIJ//33H926ddPjVxDp5PZt6NNHbX/+OVSsqG8+qalRI+jeXW137gxPnuibjxBCiMxHOhSFxRs9Wq1m5+WlVng2GPTOSFiTO3di6yYNGwZvvqlvPkJYkvPnYy/SDBggq55nFv/7X+yo/2+/VYudWZOFCxcSGBhInTp1yJ07t+n2yy+/mGKuXbtGQECA6f6jR4/o3r07JUqUoHHjxgQFBbFnzx5Kliypx68g0oGmqRInDx9C+fKqQzGjmTED8udX5SqGDtU7GyGEEJlNFr0TECIp27fDzJlqe8kS8PTUNx9hXTRNXb2/f1+Nuor5Ai2EgOBgNer7yROoVQumTdM7I5Ee/P2hXTvVPvbsqRZ3sDaapr0wxu+5OaCzZs1i1qxZaZSRsEQrVsAff4CdnZrqbGend0apz9VV1RWvV09dHGjRAt55R++shBBCZBbSoSgsh709UUuWsG/vXhZUq0ZYmD0dO6ovPd26wfvv652gsDbLlqkvE/b2qj5YRqibJDIv+6xZ2dW1KwBVX3HevqZBly5w5gzkzg2//poxv2wLc6Gh8OGHajGWKlViV70Xwto93z5ev65qJwNMmAClS+uYXBqrWxf69oV586BrVzh5UtVYFEIIIdKaQUvOZd4MJCgoCHd3dwIDA6W4toXr2hW+/x4KFoTjx9VVWCGSy99fjUoMDoapU5OeCiTtQiw5F5nDjBkwZIjqRPTzgxo19M5IpLWYTmRfX8iZE44cAR+f5D1X2gVFzoN10DRVX/Dvv6FqVdi1Sy3IkpGFhKjSBZcuQadO6oKqSB/SLgghMrMM/udVWKvff1ediQaDmqYinYkiJaKj1Qfq4GA1lXPQIL0zEsJy+PnB8OFqe9Ys6UzMLBYvVp2JNjbw88/J70x8VR4eHimKNxgMHDlyhPz586dRRiKj++471Zno6Kg+Q2b0zkQAFxf1/7tWLfVvixbQpIneWQkhhMjodP8TO3/+fKZNm8bt27cpW7Ysc+fOpUqVKonGz549m4ULF3Lt2jVy5szJRx99xKRJk3B0dEzHrEWaiIoietMmduw4QqfvKgCNGTIkC7Vq6Z2YsDazZsGOHWo1Z19fsLXVO6OXJ22kiBEVFsbRyZMBKD9iBFle4jW9cQM+/lh1unfoAL17p3aWwhIdOAD9+qntiRPh7bfT79iPHz9m9uzZuLu7vzBW0zR69+5NdHR0OmQmMpKY9vHRIxi2ZATgyMSJULy43pmlnzffhMGDYfp0VT/69GnIkUPvrIQQQmRkuk55/uWXX+jQoQOLFi2iatWqzJ49m9WrV3P+/Hly5coVL/6nn36iS5cufP/999SoUYMLFy7QqVMnWrduzcyYlTteIGZY+q1Ll3BLYNibrb09jnEKj4TcvZvovmyyZMEpzpX3lMQ+vX8fzWhMMNZgY4NzzpwvFRv68CHGqKhE83CJc15TEhv2+DHRERGpEuucMycGG7XAeHhQEFFhYeoHISG4FCqk9gcUK3aFPUfz4+ScQGwCnDw8sHl2GToiOJjIp09TJdYxWzZs7e1THBv59CkRwcGJxjq4uZk6BFISGxUWRnhQUKKx9lmzYufsnOLY6IgIwh4/TjTWztkZ+2d121ISa4yKIvThw1SJzeLoiMOz6SSa0cjT+/fjxZw9C/XrQ2iUI/MXu9GtW+KxMULCwvDKn9/ipqvo2UZa2rkQ6m+Mi5eX2r5zx6zdTY7wcKhTB/btg7JlYc8eePbfX2Rg9+5BxYpw/To0bw5r16rR/ynxKu2CjY0Nt2/fTrDNSoirqyvHjx+n0LPPA5bE9Bny1q0Ez4Otra3ZxZuQkJBE92VjY4OTk9NLxT59+jTRhWkMBgPOcf5jpyQ2NDQUYyKfNwFcXFxeKjYsLCzJTuKUxDo7O2N49gYODw8n6tnn2JC7d8ll+gx5hXI1crFjhzO2tvFjE+Lk5ITNs8+mERERREZGpkqso6Mjts+uaqYkNjIykogkPks7ODiQ5dnn2LixYWGqY/H8efjoI3VRNW5sVFQU4eHhie7X3t4eu2cFdVMSGx0dTVgSn9Ht7Oywf/b5OCWxRqOR0NDQVInNkiULDs+KaWuaxtMkPs+nJDYkJAQvLy/53CSEyJw0HVWpUkXr06eP6X50dLT22muvaZMmTUowvk+fPlq9evXMHhs0aJD25ptvJvuYgYGBGqAFqhIr8W4HPD3N4oMTidNAO+rubhZ7z2BINPa0s7NZ7HVb20RjLzo4mMVedHBINPa6ra1Z7Gln50Rj7xkMZrFH3d0TjQ1+7q1xwNMz0Vjtudg9efIkGRt8544pdmfhwgnGOD/L4d6ZM6ZYv1Klktzv9Z07TbHbKlVKMvbi+vWxsbVrJxl72tc3NrZx4yRjj86aFZtvy5ZJxh4YNy72PHTtmmTsns8+iz2/n32WZOzOrl1jX7dx45KM9WvZMvb9MGtWkrHbGjeOfZ/5+iYdW7u2Kfbi+vVJx1aqZIq9vnNn0vmWKmWKvXfmTJKxvzoX1oxGFRt8506SsVty59YALTAwULMkuraRFnYuhPn7OG47mly9e6unZ8umaZcvp0GCwuJERWla/frqdS9WTNMeP365/Ui7oMScB6dnn1OevzVt2NAsPqeTU4JxzqA1qFnTLDZfjhyJxr5ZoYJZbHEfn0RjK7z+ullshddfTzS2uI+PWeybFSokGpsvRw6z2AY1ayYam9PJySy2acOGicY6Y/4Zsk3z5knGBj95Yort+sknpsdzxPmbnuPZY3fjtJP9e/RIcr/+cRrF4Z99lmTsqRMnTLETRo9OMvbA3r2m2OkTJyYZu+2ff0yxC+bMSTJ244YNpljfxYuTjF29apUpdvWqVUnG+i5ebIrduGFDkrEL5swxxW77558kY6dPnGiKPbB3b5KxE0aPNsWeOnEiydjhcT4f+1++nGRs/x49TLF379xJMrbrJ5+YYoOfPEky9qP335f2UQiRadno1ZEZERHB4cOHqV+/vukxGxsb6tevz969exN8To0aNTh8+DAHDhwA4MqVK2zatInGjRunS85CCOvhmSvlo3AsibSRIjWtWAELFqj/Ez/+CBY4+EukgTFj4J9/1EjUdesgGbOORTLcBkISuH1+5IhZ3NXQ0ATjQoCpJ0+axR5++DDR2O/OnTOL/efWrURjf/H3N4v9xd8/0dh/bt0yi/3u3LlEYw8/N3tg6smTicZefW6U2OdHjiQa+/y4zH4HDiQdG2ekWKe9e02P/xdnH/89e8zw4IHpsRa7dye53yxxzkWjnTuTjHW4csUUW+sFsS7nz5tiK+7alWRstjjviZIviPWM814rvG9fkrHZ/Q6YYvO84PwW3rfPFOv5gtet5K5dpthsSbwfQp797jFczp9PMrbWzp2mWIcrV5KMbRQnNksS/y9CUO+BGIYHD5KM7RT3c9bTp0nG9jh8GCGEyKx0q6F4//59oqOj8Xo2fSuGl5cX55774BSjbdu23L9/n5o1a6JpGlFRUfTs2ZNRo0Ylepzw8HCz4fpBz6Z/hly6hG0CU55LPxs2b3LnTrwPOzGKP1fl2en27URjCz4X63H7NiGJTBV5zca8n/e1GzcSjfV4Lrbg9euEJDKlw+m5+8WvXEk09nmlL1wgJKkpz3G2y586lWRs3CnalY8cISQsDKMR2jUL4ff9sd9yQ65cwTNOUfZqu3cTksQUidfiTCmvsW0bIUlMTygUN/bPP5OMLR5nCnyNX35JMrZ03FhfX0LmzUs0tnycaRFVv/mGkIkTE42tHCe28sSJhIwYkWhs1WfTh0HVWQtJokhajTixpXv3JqRt28Rj40yLKt6mDSHvvpus2ELvvUfInTvJin2tWrUkY6vFmUqWo3hxs9gDB1QBcg1YugSat4yNdc6ZM8n9lgkLAwtbAEDvNlJkHMeOQY8eanvcOJD+5cxhwwaYNEltL10Kb7yhbz4xLl68yLZt27h79268KbNjx47VKavUUaFiRbP7zs7OZh1gcZUuU8bsfg4PD4jTARbX66+/bnb/tTx54Nq1BGOfnypeqFAhVQskAa/lyRP/OM91iprlF0fpMmUgTmdOXM7P1VKoULEi/PVXgrHPq1KlCvz2W6I/j7vv6tWrw+XLicbmiFNAsOabb8KpU4nGvvbaa6btWrVqwaFDicbGPce1atWC7dsTjS0ep4hjzZo1YdOmRGNLly5tHrt6daKxFSpUMG1Xq15d/SdPxOHDVainqQtKSdVgNu0rgWMkpGbNmgnm/qLY4i8obFkrTvH0F5U+iBsb9zVMMIc33zRt53hBccnqcc7D8+/n51WsWBH+/DPJGCGEyKh0q6F469Yt8uTJw549e8wa7WHDhrF9+3b2798f7zl+fn60bt2ar776iqpVq3Lp0iUGDBhA9+7dGTNmTILHGT9+PF988UW8x6XOhWWZNQtGDwohBNXB5QLcDQ42q6sjRGKCg6FcOfW9on17NRorJSyxbqC0keJ5L1ND8dEjVT/P3191JP7xh1rlV2RsFy9CpUoQFAQDBsDs2a+2v9RqIxcvXkyvXr3ImTMn3t7eplp4ELu6syUznYdEaihia6uWFo6RRF1EbGwgTl3EFMU+faom9ybEYDAvjpqS2NBQSKIuInE/k6UkNixMrQSVGrHOzrHTD8LDISqKWbPg6zF3uY/qfAq5ckW1jwnEJsrJKbZxjIiAJGodpijW0TF2ZbiUxEZGqvjEODjELl+dSOzJk1CzJoREO7D8hyy0a4c6B0nURcTeHp7VRUxRbHS0eu0SY2en4lMaazSq91pqxGbJos4bqP8TSQwQSElsUEgI7lJDUQiRSenWoRgREYGzszNr1qyhWbNmpsc7duzI48eP2bBhQ7zn1KpVi2rVqjFt2jTTYz/88AOffvopwcHBpgLJcSU0+sbHx0cafQty+rT6wmsbLh2K4uX07g0LF4KPD5w4AXEGiiaLJXYoShspnpfSDkWjUY3a3bRJTXE+dAiyZ0+PTIWeQkKgWjU1GOvNN2Hbttjv/C8rtdrI/Pnz07t3b4YPH/5qCenEEv9WZHanT0OFCpAl4i4hvPyiVRnVV1+p0gfZsqlz9YJBfOIlSLsghMjMdBunYG9vT8WKFdm6davpMaPRyNatW81G48T19OnTeF+IY1ZDS6xf1MHBATc3N7ObsBwREWpEWXg4NHxH72yENfrrL9WZCLBsWco7Ey2VtJHiVX31lepMdHRUK/tKZ2LGp2nw6aeqM9HbW82YfNXOxNT06NEjWrZsqXcaIoOIjISOHdVnyQb1XxyfGY0YoUYrP34M3bsnPlhVCCGEeBm61VAEGDRoEB07dqRSpUpUqVKF2bNnExISQufOnQHo0KEDefLkYdKzIkBNmjRh5syZlC9f3jSdb8yYMTRp0sT0pVlYly+/hKNHwcMD5i+2J3r9HHbt2sX0mjWxf76epRDPefAAunRR2/37w9tv65tPapM2UsRlnzUr2591xsStf5qQ//0Pxo9X299+q0oCiIxv3jz46Sc1a/LXXyF3br0zMteyZUv+/vtvevbsqXcqIgOYPBkOH1YXS75ZlJXtI5PXPmYmWbLA8uVqFOemTerCa8znJiGEEOJV6dqh2KpVK+7du8fYsWO5ffs25cqV46+//jItQnDt2jWz0TajR4/GYDAwevRobt68iaenJ02aNOHrr7/W61cQr2DfPohZh2TRIsidzw7696d2//7U1jc1YSX69IGAAHj9dfXFIqORNlLEZefsTO1ff31h3JUr0K6dGonSqxd06JAOyQnd7d4Ngwap7enTIc5aBbr65ptvTNtFihRhzJgx7Nu3j9KlS2P33PDJ/v37p3d6wkodPQoTJqjtefMgX2Fn8iWjfcyMSpZUF/CHDYOBA6F+fciXT++shBBCZAS61VDUi9S5sAwhIVC+vCoc364d/PCD3hkJa7NqFbRtq0bi7NunpvS8LGkXYsm5sG6hoVCjhlrZuWpVtfhoTF15kXHdvq1GIAUEQKtWqn2Ms97JK3uVdqFgwYLJijMYDFy5cuVl0ks30j5ahvBwqFxZLTry4Ydqan9qvt8zouhoeOst2LNHdShu3iwLdKUWaReEEJmZriMUReY1fLjqTMyTB+bOffZgdDTRfn6cPHmSx6VLU6tOHZmmKRJ086ZaiAVUsfFX6UwUwlpER0RwcsECAEr37o3tc2UhYkYkHjsGnp6wZo10JmYGkZGqEzEgQI1EWrLEsjpX/P399U5BZDATJqjORE9PVUPZYHhx+5jZ2dqCry+ULQv//KNmBsV8jhJCCCFeloxQFOnu77+hYcPY7QYNnv0gJASyyirPImmaBo0aqfdO5cpqmp+lrGCaEci5sFwvWuX522+hZ0816mTLFqhXT48sRXobMgRmzABXVzh4EIoXT/1jSLugyHnQ3/79ahS20agWm2rRQj3+ovZRKHPnqprTzs5w4gQULqx3RtZP2gUhRGYmg91Funr0CJ6tJ0HfvnE6E4VIpgULVGeioyOsXGlZK5gKoZf9+6FfP7U9aZJ0JmYWq1erzkRQCy+kRWdietiwYQMrVqzQOw1h4UJD1arORqMqeRLTmSiSr08fqFsXnj5Vn8eNRr0zEkIIYc2kQ1Gkq7594dYtKFYMpkzROxthbS5cgKFD1fbUqdb75VmI1HTvHnz0kZr62rx57P8RkbGdPRt7gW74cPXaW6vhw4ebVq8XIjGjR8P582r1clO5HJEiNjbw/fdqQtDOnTBnjt4ZCSGEsGbSoSjSza+/wk8/qTouK1eq6RZCJFdUlFqtNjRUFRTv00fvjITQX1QUtG4NN26oCzW+vpZVP0+kjaAg1YEYEqJGo371ld4ZvZpz584RHR2tdxrCgu3cCbNmqe3Fi8HDQ998rFmBArEjm0eNgnPndE1HCCGEFZMORZEubt1SiwUAfP45VKmibz7C+kyerKZ1ururq+uyOqEQasTOv/+CiwusWwdSvinj0zTo0kWN1MqTR63onMXKl9h7/Pgx8+bN0zsNYaGCg6FTp9j3/nvv6Z2R9eveXdUzDwtT08ijovTOSAghhDWSr+QizWkadOsGDx9CxYrqC7AQKXH4MHzxhdqePx98fPTNRwhLsG5dbOmIpUvhjTf0zUekjxkz1GIUdnZqJW9rXnti69attG3blty5czNu3Di90xEWavhwuHJF/e2fOVPvbDIGg0GtCO/uDgcOwLRpemckhBDCGkmHokhz334L//sfODjIIhoi5UJDoX17dfW8ZUtViF2IzO7SJTViB+Czz6BVK13TEenEz091rgDMng3VqumZzcu5fv06EyZMoGDBgrzzzjsYDAZ+++03bt++rXdqwgL9849ajA3U7AR3d33zyUjy5o2toThuHJw8qW8+QgghrI90KIo0dekSDB6stidPhhIlkgi2syNq4kT8GjdmwsSJ2EnPo0BNkT97Fry9YeFCqQ8nMi87Z2f8Gjfmn4aN6dDVmSdPoFYtWeAqs7h5U3UcG42qnmxMGRFrEBkZyerVq2nYsCHFixfn2LFjTJs2DRsbGz7//HMaNWqUor/5kyZNonLlyri6upIrVy6aNWvG+fPnX/i81atX8/rrr+Po6Ejp0qXZtGnTq/xaIo0FBqopzgC9e6v6yYmJaR/9GjfGTop0J1uHDvDBB2pRr44dISJC74yEEEJYE4OmaZreSaSnoKAg3N3dCQwMxE2KTaWpqCh46y3Yu1cVjd+yRereiZTZtk29dwA2boTGjdPmONIuxJJzYdk0Ddq0gV9+USudHjmiOttFxhYRAXXqqL+nZcvCnj3pu7DZq7YLuXLl4vXXX+eTTz6hZcuWZM+eHQA7OzuOHz9OyZIlU7S/Ro0a0bp1aypXrkxUVBSjRo3i1KlTnDlzBhcXlwSfs2fPHt566y0mTZrE+++/z08//cSUKVM4cuQIpUqVStZxpX1MX127qlGJhQrB8eNqZWKR+m7fViUzHj6EsWNjS8yI5JF2QQiRmVl5GW9hyaZNU19+3Nxg2TLpTBQpExgYO6Xz00/TrjNRJCzk3j1sw8LiPW5rb49jtmyxcXfvJroPmyxZcIqzFGdKYp/ev49mNCYYa7CxwTlnzpeKDX34EGMS1edd4hSkS0ls2OPHRCcxtCMlsc45c2J41mCGBwURFed1+PZb+OMXcLWBlYvAK1dOYiYbPB/7PCcPD2yerd4RERxM5NOnqRLrmC0btvb2KY6NfPqUiODgRGMd3NzI4uiY4tiosDDCg4ISjbXPmtU0giklsdEREYQ9fpxorJ2zM/bPejxSEmuMiiL04cMkYwePzMrevZAtm8bK7x6iBUcTksDpyOLoiMOzL7Sa0cjT+/cT3W9KYsOSeF8lR1RUFAaDAYPBgK2t7SvtC+Cvv/4yu+/r60uuXLk4fPgwb731VoLPmTNnDo0aNWLo0KEAfPnll2zZsoV58+axaNGiV85JpK6NG1VnosGgVq+XzsS04+2tppW3bg1ff61GLFasqHdWQgghrIKWyQQGBmqAFhgYqHcqGdrRo5pmZ6dpoGnLlyfzSVFRWtTevdppX1/twN69WlRUVFqmKCxcp07q/VOokKY9eZK2x5J2IZbpXKjBcPFuBzw9zeKDE4nTQDvq7m4We89gSDT2tLOzWex1W9tEYy86OJjFXnRwSDT2uq2tWexpZ+dEY+8ZDGaxR93dE40Nfu7P5wFPz0Rjtedi9+TJk2Rs8J07ptidhQubHo8C7fSzW1RMzmfOmGL9SpVKcr/Xd+40xW6rVCnJ2Ivr18fG1q6dZOxpX9/Y2MaNk4w9OmtWbL4tWyYZe2DcuNjz0LVrkrF7Pvss9vx+9lmSsTu7do193caNSzLWr2XL2PfDrFlJxm5r3Dj2febrm3Rs7dqm2Ivr1ycZO6rgGNNd3ymnks63VCnTfu+dOZP0eShc2BQbfOdOkrFbcufWXqWNDA0N1X744Qetbt26mpOTk9aiRQtt3bp1mp2dnXb69OmX2mdcFy9e1ADt5MmTicb4+Phos+K8/zRN08aOHauVKVMm0eeEhYVpgYGBptv169df6TyI5HnwQNNy51Zvv0GDkvecqPBw7bSvr3ba11eLCg9P2wQzqJgm+Y03NC0sTO9srId8hhRCZGYyZkykurAwtYhGZCS0aKG2k/tE2+rVKdmpE3WqV3/lERHCeq1fr0YkGAywYoWMTBACIAwo+ewmrWPmcILSzPhvFABjxsDbNR7pnNHLcXR0pF27dvz777+cPHmSEiVK0L9/f6Kiovj666/ZsmUL0dHRL7Vvo9HIwIEDefPNN5Ocunz79m28vLzMHvPy8kpyMZhJkybh7u5uuvn4+LxUjiJl+vWDgAB4/XX46qvkPSfs8WNKdupEyU6dkhwdLBK3YIFaNf70abVIixBCCPEiUkNRpLqhQ2H6dPDyUivGeXom84khIaaeIxfgbnBworWQRMZ19y6UKgX37qnVTCdPTvtjSrsQK+Zc3Lp0CTdX13g/lynPCcem5ZTn0CdhNG8OJw/e4z6qw+TuqVO4eHomOT36eTLlWbGWKc+PAw289U52rlzNQsOGagqoQUt6enRaTXkOCQvDK3/+VG0jjUYjmzdvZunSpfzxxx+4urpyP4kcEtOrVy/+97//sWvXLvLmzZtonL29PcuXL6dNmzamxxYsWMAXX3zBnTt3EnxOeHg44eHhpvtBQUH4+PjI34o0tG4dfPihKpOzdy9UqZK854XcvYvLsw7jkDt3zNpckXzr10Pz5ur8795tnSvJpzf5DCmEyMykhqJIVdu3w4wZanvx4hR0JgqBmlvXvbvqTCxTRgqD68nF0xOXZHwwTsmXtpTExu0ETM3YuJ2WqRkbt5M1NWMd3NwYPtYNv4PglRV41qfm4ukZ73w6uLmZOohexD5rVlOHll6xds7OyV6NNSWxWRwdTZ2LqRlra2+f7PdwSmJtsmSJF2s0QptucOUq5M8PP/4IqvRg/NjEGGxsUi02OolO15dlY2PDu+++y7vvvsu9e/dYuXJlivfRt29f/vzzT3bs2JFkZyKAt7d3vI7DO3fu4J3EqkYODg44ODikOC/xcu7dg5491faIEcnvTBSpp1kz+OQT+OEHterz0aPpuwCUEEII6yJTnkWqCQpSHz40Dbp1gyZN9M5IWBtfX/j9d7C3h5UrQb7Hicxu1SqYM0dtz5unby4i/UyaBH/8odrAtWshRw69M0pbnp6eDBo0KNnxmqbRt29ffvvtN/79918KFiz4wudUr16drVu3mj22ZcsWqlevnuJ8RerTNNWZeO8elC6tVhsW+vjmG3jtNbhwAT7/XO9shBBCWDLpUBSpZuBA+O8/KFgQZs7UOxthba5ehQED1PaXX6oRikJkZqdOqYszAKNGwbvv6puPSB9//63qJYKqaWbtq616eHikaCpzvnz5+O+//5KM6dOnDz/88AM//fQTrq6u3L59m9u3bxMaGmqK6dChAyNHjjTdHzBgAH/99RczZszg3LlzjB8/nkOHDtG3b9+U/1Ii1a1apaY7Z8miaifLBUX9ZM8OS5ao7TlzYMcOffMRQghhuWTKs0gVGzbAsmVqEY3lyyGB0mtCJMpoVKNbnzyBN9+EwYP1zkgIfQUGqkWtnj6F+vVhwgQIe6B3ViKtXb0KbdrEln/o0kXvjF7d48eP+d///oe7u3uy4h88ePDCBVoWLlwIQJ06dcweX7ZsGZ06dQLg2rVr2NjEXjevUaMGP/30E6NHj2bUqFEULVqU9evXJ7mQi0gft25Bnz5qe+xYKFdO13QE6gJW166wdCl06gQnTsgCeUIIIeKTDkXxyu7eVV98QC3IUquWvvkI6zN7troC7uKiOqRVrTAhMqeYDvaLFyFfPjVyR/5PZHxhYfDRR/DwIVSqpKYdZhQdO3ZM1f0lZz1BPz+/eI+1bNmSli1bpmou4tXEdJ4/fqze9yNG6J2RiDFzJmzZAv7+MGyYGjEthBBCxCUdiuKVxF1Eo3RpNYrmpdnZET16NDt37mRErVrY2dmlWp7Ccp0+raZzAsyaBYUL65uPEHqbOlWN+ra3hzVrIGbNGTtnZ/xq1waghlTJz3D694fDh1W9xDVrIJlrxVg8YyIrsAsB8P33sGmTmuK8fDm87Ec/aR9Tn5uben3q14eFC9Xqzw0a6J2VEEIIS2LQknOZNwMJCgrC3d2dwMBA3JK5GqZI3PffqykRdnZw8CCULat3RsKaRERAtWpqFcH33lOLEBgM6Z+HtAux5Fzo659/oGFDNUrxu+9iR3+LjC3mb6nBAJs3W96XdmkXFDkPqeu//9TF6CdPYNo0GDJE74xEQvr2hfnzIW9eVds3mdULMg1pF4QQmZksyiJemr+/WogF1CIa0pkoUmrCBNWZ6OEBixfr05kohKW4dk3VzzMaVe28mAVZRMZ2+DD07q22v/zS8joThUgLMe1cTO3kzz7TOyORmClT1OyRGzfkdRJCCGFOOhTFS4mOhg4dYj8IpspVZaMR48mTXNqwgdMnT8o0qQxu716YNEltf/st5M6tbz5C6Ck8XNXPu38fKlSAefPid7Abo6K4tGEDlzZswBgVpU+iIlU9eAAffqhe/w8+gDiLEguRoS1YAP/+C87O4Ov76nVipX1MOy4u6jUyGNQCjH/+qXdGQgghLIXUUBQvZdo02LVLrfi2cmUqLRgQGopNmTIUAVyAu8HBuLi4pMKOhaUJCVEd0kYjfPKJ6kgRIjMbOFCVjcieXdXPc3KKHxP68CFFmjUDIOTOHVxy5UrXHEXqio6Gdu3UtM8iRVT9OBu5zCsygQsX1CIfoEa/FSny6vuU9jFt1awJgwbBjBmqFMfp02p2iRBCiMxNPrqKFDt6FMaOVdvffAMFC+qbj7A+Q4fCpUuqHs/cuXpnI4S+fH1h0SI1+uPHH6VNzSy++ELVS3RygrVrIVs2vTMSIu1FRalV7END4e23Y6f7C8v35Zfw+utw+zb066d3NkIIISyBdCiKFAkNVSPKIiPVam+dOumdkbA2mzer1QJBTZ2RL9EiMzt6FHr1Utvjx8O77+qajkgnf/6pvpyDWnynTBl980kvtWvXZsWKFYSGhuqditDJtGmwb59aQXjZMhmVa02cnGJHUv/0E6xbp3dGQggh9Kb7lOf58+czbdo0bt++TdmyZZk7dy5VqlRJNP7x48d8/vnnrFu3jocPH5I/f35mz55N48aN0zHrzGvkSDhzBry8VN07WURDpMTDh9C5s9ru1w/q19c3H2sgbWTG9fChqp8XFqZWOR89Wu+MRHq4fBnat1fbffuqi3SZRfny5RkyZAj9+vXj448/pmvXrlSrVk3vtEQ6OX4cxo1T23Pngo+PvvmIlKtSBUaMgIkToWdPNRVaZpdbBk3TiIqKIjo6Wu9UhBBWztbWlixZsmBIRmePrh2Kv/zyC4MGDWLRokVUrVqV2bNn07BhQ86fP0+uBP46RURE0KBBA3LlysWaNWvIkycP//33H9lkiFO62LIF5sxR299/D56e+uYjrE/v3hAQAMWLw+TJemdj+aSNzLhi6of6+0OhQqoWrYzUyfiePlWdyI8fQ/Xqqh5ZZjJ79mymT5/O77//zvLly3nrrbcoUqQIXbp0oX379nh5eemdokgj4eGqIz0yEpo1i+1UF9Zn7Fg1yvrECTXCfs0aGWCgt4iICAICAnj69KneqQghMghnZ2dy586Nvb19knEGTdO0dMopnqpVq1K5cmXmzZsHgNFoxMfHh379+jFixIh48YsWLWLatGmcO3cOOzu7lzpmUFAQ7u7uBAYG4ubm9kr5ZyYPH0Lp0nDrlvrwsGBBGhwkJESt8oIsypIR/fwztGmjFvDZuxcqV9Y7o1iW2i5IG5lxffGFmuLs6Kim/5Ut++LnhNy9i8uzDhdZdMD6aJqqHbdypRrRc+QI5Mmjd1bJk1btwt27d/nuu+/4+uuviY6OpnHjxvTv35969eql2jFSk7SPL2/kSHUh0dMTTp1K/VFt0j6mr2PH1Oe4qChV+7dtW70z0o/e7YLRaOTixYvY2tri6emJvb19skYVCSFEQjRNIyIignv37hEdHU3RokWxSWLUg24jFCMiIjh8+DAjR440PWZjY0P9+vXZu3dvgs/5/fffqV69On369GHDhg14enrStm1bhg8fjm2qLDMsEqJpamTZrVtQtKiqfyNESty8GVsnbvRoy+pMtFTSRmZcmzapDkVQpSOS05korN+iRaoz0dYWfvnFejoT08qBAwdYtmwZP//8M7ly5aJTp07cvHmT999/n969ezN9+nS9UxSpZM8emDpVbX/7rUyRzQjKlVMjFceOVaUb6tSB117TO6vMKSIiwnTB2dnZWe90hBAZgJOTE3Z2dvz3339ERETg6OiYaKxuHYr3798nOjo63vQWLy8vzp07l+Bzrly5wr///ku7du3YtGkTly5donfv3kRGRjIupijLc8LDwwkPDzfdDwoKSr1fIpNYtUp9+bG1hR9+gDQbNGhnR/Rnn7Fz50761ar10iOshGXRNOjSRU3xq1QJPv9c74ysg7SRGdOVK2qqc8yFmg4dkv9cO2dn/CpVAqCGfGmwKvv2wYABanvKFPXlOzO6e/cuK1euZNmyZVy8eJEmTZqwatUqGjZsaBpR06lTJxo1aiQdihlESIgamWs0qvauefO0OY60j+lvxAjYsAEOH4ZPP4U//pCpz3pKagSREEKkVHLbFN0XZUkJo9FIrly5+O6777C1taVixYrcvHmTadOmJfpledKkSXwRMxREpNj169Cnj9oeO1YVY04z9vbYzpxJHaBOGh5GpK+FC+Hvv9XUzpUrQfqJ0460kZYtNFTVz3v0CKpWhVmzUvZ8+6xZqXPwYNokJ9LM3bvw0UeqdtxHH8GgQXpnpJ+8efNSuHBhunTpQqdOnfBMoBhzmTJlqCzD2DOM4cPh0iXImze2DndakPYx/dnZqVWfK1SAjRvB1zd24T0hhBCZg26XMnLmzImtrS137twxe/zOnTt4e3sn+JzcuXNTrFgxs6l7JUqU4Pbt20RERCT4nJEjRxIYGGi6Xb9+PfV+iQzOaFQfDB4/Vh2Jo0bpnZGwNhcvwpAhanvKFHj9dX3zsSbSRmYsmqam/R87pmqIrVkDL6hxLDKAqCho3VqVfXj9dbWgWWYewbN161bOnj3L0KFDE+xMBHBzc2Pbtm3pnJlIC//8A/Pnq+1ly0DWB8t43ngDvvxSbQ8cCNeu6ZqOEAAYDAbWr1+frNjx48dTrly5JGPq1KnDwIEDXzmv9HT16lUMBgPHjh3TO5VX4ufnh8Fg4PHjx3qnIhKhW4eivb09FStWZOvWrabHjEYjW7dupXr16gk+58033+TSpUsYjUbTYxcuXEhy9RkHBwfc3NzMbiJ55s6FrVvB2VmNLMuS1uNZjUaMV65wY9curl65YvY6C+sTFaVWcQwNhbffVjV2RPJJG5mxfPedGslhY6NKSOTNm/J9GKOiuLFrFzd27cIYFZX6SYpU9/nnsG2bWm9s3TpwddU7I32NGzcuwS8FQUFBFrsQi3g5jx/Hjlbr3Rvq10/b40n7qJ/Bg9Wq9UFB0LWruoAmxIvcu3ePXr16kS9fPhwcHPD29qZhw4bs3r3bFJOSjsG4AgICePfdd1Mt13Xr1vFlTM+5jnx9fcmWzCszPj4+BAQEUKpUqbRNSmR6uhZbGDRoEIsXL2b58uWcPXuWXr16ERISQudnn0A6dOhgtiBBr169ePjwIQMGDODChQts3LiRiRMn0idmTq5INWfOqGkqADNmQLFi6XDQ0FBsChcmb61avFG4MKGhoelwUJFWpkyB/fvB3V2NTJDSLiknbWTGsH8/9OuntidPhrp1X24/oQ8fkrdWLfLWqkXow4epl6BIE+vWxS5E8f33UKKEvvlYgu3btyc4WjosLIydO3fqkJFIKwMHwo0bUKRI7P+DtCTto35sbdV0ZycnNSp10SK9MxLW4MMPP+To0aMsX76cCxcu8Pvvv1OnTh0ePHjwyvv29vbGwcEhFbJUPDw8cLWiK4IRERHY2tri7e1NljQfESQyO12/4rdq1Yrp06czduxYypUrx7Fjx/jrr79MixBcu3aNgIAAU7yPjw+bN2/m4MGDlClThv79+zNgwABGjBih16+QIUVEqEUDwsPh3XehRw+9MxLW5sgRGD9ebc+dCz4+uqZjtaSNtH737sXWz2vRIrYEgMjYzp2DTp3U9qBB0LKlruno7sSJE5w4cQJN0zhz5ozp/okTJzh69ChLly4lT2Zf9joD2bAhdkT28uVpuJifsBjFisGkSWp76FC1AJkQiXn8+DE7d+5kypQp1K1bl/z581OlShVGjhzJBx98AECBAgUAaN68OQaDwXQfYOHChRQuXBh7e3uKFy/OypUrzfb//MjGGzdu0KZNGzw8PHBxcaFSpUrs37/f7DkrV66kQIECuLu707p1a548eWL62fNTnh89ekSHDh3Inj07zs7OvPvuu1y8eNH085iRhH/++SfFixfH2dmZjz76iKdPn7J8+XIKFChA9uzZ6d+/P9HR0abnhYeHM2TIEPLkyYOLiwtVq1bFz88PUFN/O3fuTGBgIAaDAYPBwPhnX7YKFCjAl19+SYcOHXBzc+PTTz9NcMrz6dOnef/993Fzc8PV1ZVatWpx+fLlRF+nU6dO8e6775I1a1a8vLxo37499+/fNzsv/fv3Z9iwYXh4eODt7W3KCaBt27a0atXKbJ+RkZHkzJmTFStWAGr21aRJkyhYsCBOTk6ULVuWNWvWJJoTwNq1a3njjTdwcHCgQIECzJgxw+znMeejTZs2uLi4kCdPHubH1N945vHjx3Tr1g1PT0/c3NyoV68ex48fT/K4IhFaJhMYGKgBWmBgoN6pWKxRozQNNC1HDk27dSsdDxwcrA4MmjNowcHB6XhwkVpCQzWtZEn1Un74oaYZjXpn9GLSLsSSc5F6IiM1rV499X+heHFNe9VTGnznjqmNDL5zJ3WSFKnuyRNNK1FCvVS1a6v3gbV71XbBYDBoNjY2mo2NjWYwGOLdnJ2dtaVLl6Zy1qlP2scXu3tX03LlUu//4cPT77jSPuovOlq1eaBpb72l7mcGercLoaGh2pkzZ7TQ0FDTY0aj+lqV3rfkfuaPjIzUsmbNqg0cOFALCwtLMObu3bsaoC1btkwLCAjQ7t69q2mapq1bt06zs7PT5s+fr50/f16bMWOGZmtrq/3777+m5wLab7/9pmmapj158kQrVKiQVqtWLW3nzp3axYsXtV9++UXbs2ePpmmaNm7cOC1r1qxaixYttJMnT2o7duzQvL29tVGjRpn2V7t2bW3AgAGm+x988IFWokQJbceOHdqxY8e0hg0bakWKFNEiIiI0TdO0ZcuWaXZ2dlqDBg20I0eOaNu3b9dy5MihvfPOO9rHH3+snT59Wvvjjz80e3t77eeffzbtt1u3blqNGjW0HTt2aJcuXdKmTZumOTg4aBcuXNDCw8O12bNna25ublpAQIAWEBCgPXnyRNM0TcufP7/m5uamTZ8+Xbt06ZJ26dIlzd/fXwO0o0ePapqmaTdu3NA8PDy0Fi1aaAcPHtTOnz+vff/999q5c+cSPP+PHj3SPD09tZEjR2pnz57Vjhw5ojVo0ECrW7eu2Xlxc3PTxo8fr124cEFbvny5ZjAYtL///lvTNE37888/NScnJ1OemqZpf/zxh+bk5KQFBQVpmqZpX331lfb6669rf/31l3b58mVt2bJlmoODg+bn56dpmqZt27ZNA7RHjx5pmqZphw4d0mxsbLQJEyZo58+f15YtW6Y5OTlpy5YtMx0jf/78mqurqzZp0iTt/Pnz2jfffKPZ2tqa8tI0Tatfv77WpEkT7eDBg9qFCxe0wYMHazly5NAePHiQ4PnIjBJqWxIiHYrCzO7dmmZjoz4MrF2bzgeXDsUMYdAg9TJ6eWnavXt6Z5M80i7EknORekaMUP8XXFw07fTpV9+ffGG2fEajprVqpV6m117TtNu39c4odbxqu3D16lXN399fMxgM2sGDB7WrV6+abrdu3dKioqJSOeO0Ie1j0oxGTWvRQr3/S5XStET6CdKEtI+W4fJl9TcPNG3WLL2zSR96twsJfemP85UqXW8p+eq2Zs0aLXv27Jqjo6NWo0YNbeTIkdrx48fNYuJ2DMaoUaOG1r17d7PHWrZsqTVu3DjB53377beaq6troh1F48aN05ydnU0dXJqmaUOHDtWqVq1quh+3Q/HChQsaoO3evdv08/v372tOTk7ar7/+qmma6lAEtEuXLplievTooTk7O5t1rjVs2FDr0aOHpmma9t9//2m2trbazZs3zfJ7++23tZEjR5r26+7uHu93yJ8/v9asWTOzx57vUBw5cqRWsGBBU6fni3z55ZfaO++8Y/bY9evXNUA7f/686bzUrFnTLKZy5cra8GdXkyIjI7WcOXNqK1asMP28TZs2WqtWrTRN07SwsDDN2dnZ1Lkbo2vXrlqbNm00TYvfodi2bVutQYMGZvFDhw7VSpYsaXY+GjVqZBbTqlUr7d1339U0TdN27typubm5xevMLly4sPbtt9++4MxkHsntUJSqZsIkOFgtomE0QocOanqeECnh5wezZqntJUsgZ05d0xFCN7/9puolgqqfV7KkvvmI9DFnjlp0J0sWWL0anlUnyPTy589PgQIFMBqNVKpUifz585tuuXPnNluZXlivn35StUOzZIEVKyAVS5gJK1GoEEyfrrZHjoTz5/XNR1iuDz/8kFu3bvH777/TqFEj/Pz8qFChAr6+vkk+7+zZs7z55ptmj7355pucPXs2wfhjx45Rvnx5PDw8Et1ngQIFzGok5s6dm7t37yZ6/CxZslC1alXTYzly5KB48eJmOTg7O1O4cGHTfS8vLwoUKEDWrFnNHos5zsmTJ4mOjqZYsWJkzZrVdNu+fXuS05JjVKpUKcmfHzt2jFq1amFnZ/fCfQEcP36cbdu2meXy+uuvA5jlU6ZMGbPnxT13WbJk4eOPP+bHH38EICQkhA0bNtCuXTsALl26xNOnT2nQoIHZcVasWJHo75zY63/x4kWz6ePPL2BZvXp10+tz/PhxgoODyZEjh9lx/f39k3WuhTmp0ilMBg9WNU/y5YNvvtE7G2FtgoJUzTBNg27d4P339c5ICH1cuAAdO6rtQYPg44/1zUekj507Y2tkzpoFNWrom4+l+P3333n33Xexs7Pj999/TzI2pnaWsD43b0Lfvmp77FgoX17ffIR+evRQHctbtqjPhTt3qk5mkX6cndVAET2OmxKOjo40aNCABg0aMGbMGLp168a4cePoFFOEOBU4OTm9MOb5TjaDwYDRaHyl4ya0z6SOExwcjK2tLYcPH453kS1uJ2RiXF5QrDY55yGu4OBgmjRpwpQpU+L9LHfu3KbtF527du3aUbt2be7evcuWLVtwcnKiUaNGpmMAbNy4MV4d5dRcVOd5wcHB5M6d21SfMq7krqItYknzLgDYtAm++05t+/qqlXmFSIkBA+C//6BgQZg5U+9shNBHcLAa3f3kCbz1VuwoRZGxBQSojuPoaGjXDmRh9VjNmjXj9u3b5MqVi2bNmiUaZzAYzEYXvMiOHTuYNm0ahw8fJiAggN9++y3J/fv5+VE3gSXWAwIC8Pb2TvZxRXyaBt27w+PHULmyGpkmMi+DAZYuhVKlYN8+NWJR1oZLXwaDdS6GVLJkSbPFVOzs7OL9XShRogS7d++mY8yVW2D37t2UTGQqSJkyZViyZAkPHz5McpRicpUoUYKoqCj2799PjWdXDh88eMD58+cTzSE5ypcvT3R0NHfv3qVWrVoJxtjb26fo72RcZcqUYfny5URGRiZrlGKFChVYu3YtBQoUeKWVomvUqIGPjw+//PIL//vf/2jZsqXp+CVLlsTBwYFr165Ru3btZO0v5vWPa/fu3RQrVsysI3bfvn1mMfv27aNEiRKm3+327dtkyZLFbLEf8XJkyrPg/n3o2lVtDxwICXzeTh9ZshDdowfbS5WiW48essy9FVm/XnVEGwxqRcc4swaEyDRiRueePg25c6upr8mcWZIsWRwd2V6qFNtLlSKLo2Pq7Vi8kshI1Zl4+zaULg3ffqvaQqEYjUZy5cpl2k7sltIvSSEhIZQtWzbeyo0vcv78eQICAky3mNzEy1u6FP73PzXFeflyfUajSftoWXx8VAkIgHHj4NQpffMRluXBgwfUq1ePH374gRMnTuDv78/q1auZOnUqTZs2NcUVKFCArVu3cvv2bR49egTA0KFD8fX1ZeHChVy8eJGZM2eybt06hsRMEXhOmzZt8Pb2plmzZuzevZsrV66wdu1a9u7d+1K5Fy1alKZNm9K9e3d27drF8ePH+eSTT8iTJ49Z7ilVrFgx2rVrR4cOHVi3bh3+/v4cOHCASZMmsXHjRkCdj+DgYLZu3cr9+/d5+vRpsvfft29fgoKCaN26NYcOHeLixYusXLmS84nUJejTpw8PHz6kTZs2HDx4kMuXL7N582Y6d+6c4r/Xbdu2ZdGiRWzZssU03RnA1dWVIUOG8Nlnn7F8+XIuX77MkSNHmDt3LsuXL09wX4MHD2br1q18+eWXXLhwgeXLlzNv3rx4r//u3buZOnUqFy5cYP78+axevZoBAwYAUL9+fapXr06zZs34+++/uXr1Knv27OHzzz/n0KFDKfrdBLLKc2ZnNGraRx+pQrolSmja06d6ZySszZ07mubpqd5Dw4bpnc3LkXYhlpyLlzd7tvp/kCWLpu3apXc2Ir0MGKBedzc3TbtwQe9s0oaltwskULj/ec8Xdn8Zln4e9ODvr2lZs6r/A9Om6Z2NsCRGo6a9/756b5Qvr2nJXAvC6ujdLiR34QRLEhYWpo0YMUKrUKGC5u7urjk7O2vFixfXRo8erT2N82X0999/14oUKaJlyZJFy58/v+nxBQsWaIUKFdLs7Oy0YsWKmS36oWnx/yZcvXpV+/DDDzU3NzfN2dlZq1SpkrZ//35N09SiLGXLljV7/qxZs8yO9/wqzw8fPtTat2+vubu7a05OTlrDhg21C3E+ACS0eEpCx+nYsaPWtGlT0/2IiAht7NixWoECBTQ7Ozstd+7cWvPmzbUTJ06YYnr27KnlyJFDA7Rx48ZpmqYWIZn13CpIzy/Kommadvz4ce2dd97RnJ2dNVdXV61WrVra5cuXtcRcuHBBa968uZYtWzbNyclJe/3117WBAwdqxmfLeT9/XjRN05o2bap17NjR7LEzZ85ogJY/f37Tc2MYjUZt9uzZWvHixTU7OzvN09NTa9iwobZ9+3ZN0xL+271mzRqtZMmSmp2dnZYvXz5t2nN/fPLnz6998cUXWsuWLTVnZ2fN29tbmzNnjllMUFCQ1q9fP+21117T7OzsNB8fH61du3batWvXEj0fmU1y2xaDpmmaLj2ZOgkKCsLd3Z3AwEDc3Nz0Tkd3P/4In3yiribv2wcVK+qdkbAmmgbNm8OGDWpkzsGD1lmEXdqFWHIuXs7OnVCvHkRFqRq0/frpnZFID6tWQdu2anvDBsioJQBTq13o378/RYoUoX///maPz5s3j0uXLjF79uyX2q/BYEj2lOf8+fMTHh5OqVKlGD9+fLzi7kmR9tGc0Qj168O2bfDmm7B9O8j6OiKugAB44w149AjGj1ejFTMavduFsLAw/P39KViwII4yOlcIChQowMCBAxk4cKDeqVi15LYtMuU5E7t+PbbO09ixFtCZqGlod+9y/+xZ7t29Sybr67ZKvr7qS7SdHaxcaZ2diUK8qpj6eVFR0KZN7MIEqU0zGrl/9iz3z55Fe8Vi4eLVnTqlprgDjBqVcTsTU9PatWsT7MCrUaMGa9asSdNj586dm0WLFrF27VrWrl2Lj48PderU4ciRI4k+Jzw8nKCgILObiLVggepMdHZWnwf07EyU9tEy5c4NMVUJvvoKkvjvJoQQwgpJkbpMymiEzp0hMBCqVrWQAtpPn2Lw8iIn4ALcDQ5+4YpVQj9Xr6qFWAAmTICyZXVNRwhdREZCy5aqft4bb8DixWlXP+/p/fvkfFbwO+TOHVyk9ptuAgPV4jtPn6oRWhMm6J2RdXjw4AHuCaz65ubmxv3799P02MWLF6d48eKm+zVq1ODy5cvMmjWLlStXJvicSZMm8cUXX6RpXtbq4kUYNkxtT50KRYrom4+0j5ardWtYu1bdOnaEQ4fkArQQQmQUMkIxk5o/H7ZuBScnWLFCnwLawnoZjdCpk1rJtkYNGDpU74yE0MfQobB7N7i5wbp11rmqokgZo1F9Kb54EfLlU9OeZZpn8hQpUoS//vor3uP/+9//KFSoULrnU6VKFS5dupToz0eOHElgYKDpdv369XTMznJFR6vPAKGhqtRDr156ZyQsmcEACxeCp6ca2T1+vN4ZCSEysqtXr8p053SU4g7Fbdu2Jfqzb7/99pWSEenj3LnYq8rTpkGxYvrmI6zP7NmqVpKLi+qQli/TsTp27MiOHTv0TkOkg1WrYlexXLFC2tLMYupUVerB3h7WrIGcOfXOyHoMGjSIYcOGMW7cOLZv38727dsZO3YsI0aM4LPPPkv3fI4dO0bu3LkT/bmDgwNubm5mNwEzZ8KePeDqCt9/DzYyPEG8gKcnxHxNnDpV1W0XQghh/VL8EaBRo0YMHTqUyMhI02P379+nSZMmjBgxIlWTE6kvMhLat4ewMGjQAHr31jsjYW1On1b1wkB9qShcWN98LE1gYCD169enaNGiTJw4kZs3b+qdkkgDp0+b189r2lTffET62LoVPv9cbc+bB5Ur65uPtenSpQszZsxg6dKl1K1bl7p16/LDDz+wcOFCunfvnqJ9BQcHc+zYMY4dOwaAv78/x44d49q1a4AaXdihQwdT/OzZs9mwYQOXLl3i1KlTDBw4kH///Zc+McWkRbKcOQNjxqjtWbMgf3598xHWo3lzaNcudpZLaKjeGQkhhHhVLzVC8bfffqNy5cqcOXOGjRs3UqpUKYKCgkwf6oTlmjhR1S7Jlg2WLUu7Wl8iY4qIUB3S4eHQuDGk8PtfprB+/Xpu3rxJr169+OWXXyhQoADvvvsua9asMbsQI6yX1M/LnK5dU7XAjEbo0iW2Q1mkTK9evbhx4wZ37twhKCiIK1eumHX8JdehQ4coX7485cuXB9Tox/LlyzN27FgAAgICTJ2LABEREQwePJjSpUtTu3Ztjh8/zj///MPbb7+dOr9YJhAZqab7x3wG6NJF74yEtZk7Vy3Ucv587MUZIYQQ1sugvcRSusHBwfTs2ZM1a9ZgNBr58ssvGTZsGAYr6J0KCgrC3d2dwMDATDd15dAhqFZN1b756Se1GqlFCQmBrFkBWZTFUo0eDV9/DTlywMmT6kNhRpCW7cKRI0dYtmwZS5YsIWvWrHzyySf07t2bokWLpupxUktmbiOTQ9NUZ+L69ap+3uHD6TflNeTuXVy8vNS2LDqQrsLDoVYtOHgQKlSAXbtUDeLMQtoFJbOfhy+/hLFj1UXp06fhtdf0ziiWtI/WY9MmeO89Nahh+3bVtlozvduFsLAw/P39KViwII6Ojul+fCFExpTctuWlqp5cuHCBQ4cOkTdvXrJkycL58+d5+vTpSycr0l5oqBpZFh0NH3+sRlkIkRL79sGkSWp70aKM05mYlgICAtiyZQtbtmzB1taWxo0bc/LkSUqWLMmsWbP0Tk+8hKlTVWei1M/LXAYOVJ2J2bOr1z0zdSamtjVr1vDxxx9TrVo1KlSoYHYTluvYsdjR2PPmWVZnorAujRtD167qAl3HjhAcrHdGQgghXlaKOxQnT55M9erVadCgAadOneLAgQMcPXqUMmXKsHfv3rTIUaSCkSPVYiy5c6uV1ixyMGmWLER/8gm7ChemzSefkEWWnrYYISGqQ9poVPVvPvpI74wsV2RkJGvXruX9998nf/78rF69moEDB3Lr1i2WL1/OP//8w6+//soEmSdrdbZuja0fqkf9vCyOjuwqXJhdhQuTRUYhpBtfX3URxWCAH3+EggX1zsh6ffPNN3Tu3BkvLy+OHj1KlSpVyJEjB1euXOHdd9/VOz2RiIgI1fETFaXq4LVtq3dG8Un7aF1mzlSj/P39YxeKFEIIYX1S3KE4Z84c1q9fz9y5c3F0dKRUqVIcOHCAFi1aUKdOnTRIUbyqf/+NXYn0++/Bw0PffBLl4IDtypXUvHSJJStX4uDgoHdG4plhw+DSJciTR3WkiMTlzp2b7t27kz9/fg4cOMChQ4fo2bOn2TSYunXrki1bNv2SFCl2/Xps/bzOnfWpn+fg5kbNS5eoeekSDplwuqUejh6FXr3U9vjxIH1er2bBggV89913zJ07F3t7e4YNG8aWLVvo378/gYGBeqcnEjFhApw4oUZkx3SuWxppH62Lm5v6TgJqoMM//+ibj7Bevr6+qfqZ+urVqxgMhldeGyK19pMc48ePx8vLC4PBwPr169P8eHry8/PDYDDw+PHjZD+nTp06DBw4MMmYAgUKMHv27JfO6/nXO7l5vui46fk+elkp7lA8efJkvKvIdnZ2TJs2jb///jvVEhOp4/FjdVUZoGdPaNRI13SEFdq8GRYsUNu+vqp2kkjcrFmzuHXrFvPnz6dcuXIJxmTLlg1/f//0TUy8tPBwNSr3/n1VP2/+fMv8Qi1S18OH8OGHEBampuiNHq13Rtbv2rVr1KhRAwAnJyeePHkCQPv27Vm1apWeqYlEHDgAkyer7YULQUoTitTy9tvQu7fa7tJFLXgmMpfbt2/Tr18/ChUqhIODAz4+PjRp0oStW7fqnVqKdOrUiWbNmpk95uPjQ0BAAKVKlUrTY589e5YvvviCb7/9loCAABntbyFq1KhBQEAA7u7uwMt3fKfX++hVpLhDMWcSBaNq1679SsmI1Ne/P9y4AYULw/TpemfzApqGFhxMyN27hAQH8xLrBYlU9vBh7CqO/fqpFW1F0tq3by9FsTOYgQPVl2oPD1i7Vr/6eZrRqNrHu3fRjEZ9ksgkjEZV5sHfHwoVgh9+AJuXqjot4vL29ubhw4cA5MuXj3379gHg7+8vf/MtUGgodOqk6m+3aWPZ5U6kfbROU6aoNvb6dRg0SO9sRHq6evUqFStW5N9//2XatGmcPHmSv/76i7p169KnTx+903tltra2eHt7p3kJr8uXLwPQtGlTvL29E5zhFxERkaY5iPjs7e3x9vZ+5UWL0+t99Crk43EGtnYtrFypvgStXAkWv2Dy06cYXF1x8fIil6urLPRjAfr0gVu3oHjx2BEKQmQmz9fPK1BAv1ye3r+Pi5cXLl5ePL1/X79EMoEvv1QrkTo6qr+l2bPrnVHGUK9ePX7//XcAOnfuzGeffUaDBg1o1aoVzZs31zk78bwxY+DsWfD2hrlz9c4madI+WqesWdXfWYNBTYHeuFHvjER66d27NwaDgQMHDvDhhx9SrFgx3njjDQYNGmS62AQwc+ZMSpcujYuLCz4+PvTu3ZvgF6zk88cff1C5cmUcHR3JmTOn2d+XhKYFZ8uWDV9f3wT3FR0dTdeuXSlYsCBOTk4UL16cOTG1xFDTjZcvX86GDRswGAwYDAb8/PwSnKq6fft2qlSpgoODA7lz52bEiBFERUWZfl6nTh369+/PsGHD8PDwwNvbm/Hjxyf6e44fP54mTZoAYGNjY+q8ihkx+fXXX/Paa69RvHhxQM00rVevHk5OTuTIkYNPP/3U7FzGPG/ixIl4eXmRLVs2JkyYQFRUFEOHDsXDw4O8efOybNmyJM+/0Whk6tSpFClSBAcHB/Lly8fXX38NqM8Bffv2NYu/d+8e9vb2ppGp4eHhDB8+HB8fHxwcHChSpAhLly5N8FgPHjygTZs25MmTB2dnZ0qXLp3gjIeoqCj69u2Lu7s7OXPmZMyYMUleyHz8+DHdunXD09MTNzc36tWrx/Hjx5P8veOKO+XZz8+Pzp07ExgYaHqPxH1dnz59SpcuXXB1dSVfvnx89913pp89/z5KaKTj+vXrzToux48fT7ly5fj+++/Jly8fWbNmpXfv3kRHRzN16lS8vb3JlSuX6TV5VZbb1Sleye3b0KOH2h4xAqpX1zcfYX1+/lndbG1hxQpwdtY7IyHSV9z6eV98ISUjMov//U+93qA6kxOpXCBewnfffYfx2eixPn36kCNHDvbs2cMHH3xAj5gPLcIi7NqlFs4AWLwYcuTQNx+RcdWqpWYCzJoF3bvDqVMWXO/dyoSEhCT6M1tbW7MZNUnF2tjY4BRnekZCsS4pGLny8OFD/vrrL77++usEnxe3w8TGxoZvvvmGggULcuXKFXr37s2wYcNYEFOP6TkbN26kefPmfP7556xYsYKIiAg2bdqU7NyeZzQayZs3L6tXrzb9zfr000/JnTs3H3/8MUOGDOHs2bMEBQWZOto8PDy4deuW2X5u3rxJ48aN6dSpEytWrODcuXN0794dR0dHs86l5cuXM2jQIPbv38/evXvp1KkTb775Jg0aNIiX25AhQyhQoACdO3cmICDA7Gdbt27Fzc2NLVu2AOo1a9iwIdWrV+fgwYPcvXuXbt260bdvX7PO1H///Ze8efOyY8cOdu/eTdeuXdmzZw9vvfUW+/fv55dffqFHjx40aNCAvHnzJnjORo4cyeLFi5k1axY1a9YkICCAc+fOAZiOOWPGDNNoyh9++IE8efJQr149ADp06MDevXv55ptvKFu2LP7+/txP5EJRWFgYFStWZPjw4bi5ubFx40bat29P4cKFqVKlitl57dq1q6m+/aeffkq+fPno3r17gvtt2bIlTk5O/O9//8Pd3Z1vv/2Wt99+mwsXLuCRwgaqRo0azJ49m7Fjx3L+/HkAsmbNavr5jBkz+PLLLxk1ahRr1qyhV69e1K5d29QR/DIuX77M//73P/766y8uX77MRx99xJUrVyhWrBjbt29nz549dOnShfr161O1atWXPg4AWiYTGBioAVpgYKDeqaQZo1HT3ntP00DTypXTtPBwvTNKpuBglTRozqAFBwfrnVGmdeOGpmXPrl6OsWP1zibtZYZ2IbnkXCgPHmhawYLq/8B772ladLTeGWla8J07pjYy+M4dvdPJkK5ciW37evXSOxvLIe2CklnOQ3CwphUurP4fdOqkdzbJI+2jdXv6VNOKF1cvYbt2emeTMnq3C6GhodqZM2e00NDQeD8DEr01btzYLNbZ2TnR2Nq1a5vF5syZM15MSuzfv18DtHXr1qX49129erWWI0cO0/1ly5Zp7u7upvvVq1fX2iXxJgK03377zewxd3d3bdmyZZqmaZq/v78GaEePHk10H3369NE+/PBD0/2OHTtqTZs2NYt5fj+jRo3SihcvrhmNRlPM/PnztaxZs2rRzz5k1q5dW6tZs6bZfipXrqwNHz480Vx+++23eOe/Y8eOmpeXlxYepxPgu+++07Jnz272/Xrjxo2ajY2Ndvv2bdPz8ufPb8pH0zStePHiWq1atUz3o6KiNBcXF23VqlUJ5hMUFKQ5ODhoixcvTvDnoaGhWvbs2bVffvnF9FiZMmW08ePHa5qmaefPn9cAbcuWLQk+f9u2bRqgPXr0KMGfa5qmvffee9rgwYNN92vXrq2VKFHC7NwPHz5cK1GihOl+/vz5tVmzZmmapmk7d+7U3NzctLCwMLP9Fi5cWPv2228TPObzr/fzeT7/Po173E8++cR032g0arly5dIWLlyY4H4T2s/z74Fx48Zpzs7OWlBQkOmxhg0bagUKFIj32k6aNCnB30fTkm5b4pIRihnQkiVqyoC9vZrqbG+vd0bCmmgadO0Kjx5BxYqyEIHIfIxG+OST2Pp5MaUjRMYWGqoWYXn0CKpWVaNlROp79OgRS5cu5ezZswCULFmSzp07p/iKv0g7I0bA5cvg4wOvsOilEMnm5ATLl0ONGqq8SIsW6iYyJi0FNXP/+ecfJk2axLlz5wgKCiIqKoqwsDCePn2KcwLTp44dO5boqLOXNX/+fL7//nuuXbtGaGgoERERiS68mJizZ89SvXp1s6mpb775JsHBwdy4cYN8+fIBUKZMGbPn5c6dm7t376Y459KlS2MfpxPg7NmzlC1b1mxE6JtvvonRaOT8+fN4eXkB8MYbb2AT50Ovl5eX2YIgtra25MiRI9Gczp49S3h4OG+//XaCP3d0dKR9+/Z8//33fPzxxxw5coRTp06ZyqEcO3YMW1vbZK/NER0dzcSJE/n111+5efMmERERhIeHx3tvVKtWzezcV69enRkzZhAdHY2tra1Z7PHjxwkODibHc0PzQ0NDTTUrU1Pc19xgMODt7f1Sr3lcBQoUwNXV1XTfy8sLW1vbeK/tqx4HZMpzhnPlCnz2mdqeOBEseEEgYaEWLVIrOzs6qo4UOzu9MxIifX35pZr2KvXzMg9NU6uNHj0Knp6wZg0kUNdcvKIdO3bwwQcf4ObmRqVKlQD45ptvmDBhAn/88QdvvfWWzhmKrVth3jy1vXQpPFugUog0V7UqDB8OkyZBz55qKrSnp95ZWbekag0+34mSVMeCzXNXVa9evfpKeRUtWhSDwWCaBpuYq1ev8v7779OrVy++/vprPDw82LVrF127diUiIiLBDkWnF6ycZzAY4nVoRkZGJhr/888/M2TIEGbMmEH16tVxdXVl2rRp7N+/P8njvCy75754GQwGU6mQlEjJFPQXHT8lOb3o/IOa9lyuXDlu3LjBsmXLqFevHvnz50/28+OaNm0ac+bMYfbs2aZamwMHDnylhWiCg4PJnTs3fn5+8X72Mis1v0hKzq+NjU2y3r+v+jqmhIy5yECio6FjRwgJgbfeUvVIhEiJixdhyBC1PXkylCihbz5CpLdNm2Lr5337rdTPyywWL1YLA9jYqNqxiZQFEq+oT58+fPzxx/j7+7Nu3TrWrVvHlStXaN26dYZY1dPaBQVBly5qu1cvSKBklxBpatw4KF0a7t1T70FZ/P3VuLi4JHqLWz/xRbHPd/IkFJMSHh4eNGzYkPnz5ydYj/Hx48cAHD58GKPRyIwZM6hWrRrFihWLV5vweWXKlDEt7pEQT09Ps3qDFy9eTHIh0N27d1OjRg169+5N+fLlKVKkSLxRavb29kRHRyeZV4kSJdi7d69ZZ9Du3btxdXVNtBZhaipRogTHjx83O9+7d+/GxsbmlWr1Pa9o0aI4OTkl+RqULl2aSpUqsXjxYn766Se6xPzhefYzo9HI9u3bk3W83bt307RpUz755BPKli1LoUKFuHDhQry45zuA9+3bR9GiReN1rANUqFCB27dvkyVLFooUKWJ2y5kzZ7Lyel5y3iPJ4enpyZMnT8xex7gL/+hBOhQzkOnTVRFtV1c1bSCB/x9CJCoqCjp0gKdPoW5d6NdP74yESF9XrkC7duoLTK9e6v+DyPgOHIht7yZOhGc1wUUauHTpEoMHDzb7AG9ra8ugQYO4dOmSjpkJgMGD4do1Veph6lS9sxGZkYOD+g6TJYuaIfDzz3pnJNLK/PnziY6OpkqVKqxdu5aLFy9y9uxZvvnmG6o/W020SJEiREZGMnfuXK5cucLKlStZtGhRkvsdN24cq1atYty4cZw9e5aTJ08yZcoU08/r1avHvHnzOHr0KIcOHaJnz57xRm7FVbRoUQ4dOsTmzZu5cOECY8aM4eDBg2YxBQoU4MSJE5w/f5779+8nOGKsd+/eXL9+nX79+nHu3Dk2bNjAuHHjGDRoULwRoGmhXbt2ODo60rFjR06dOsW2bdvo168f7du3N013Tg2Ojo4MHz6cYcOGsWLFCi5fvsy+ffvirdLcrVs3Jk+ejKZpZqtwFyhQgI4dO9KlSxfWr1+Pv78/fn5+/Prrrwker2jRomzZsoU9e/Zw9uxZevTowZ07d+LFXbt2jUGDBnH+/HlWrVrF3LlzGTBgQIL7rF+/PtWrV6dZs2b8/fffXL16lT179vD5559z6NChlzovBQoUIDg4mK1bt3L//v0kO7GTUrVqVZydnRk1ahSXL1/mp59+SnSF8vQiHYoZxPHjMGaM2p4zBwoU0DWdl2NrS3Tz5uzNk4emzZsneMVApJ2pU2HfPnBzix2pI0RmEVM/7/Fjy62fZ2tvz948edibJw+2Uhw3Vdy7Bx99BBER0Lw5DBumd0YZW4UKFUy1E+OKqe0k9PO//6ka3AYDLFsGcRagtArSPmYc5cvH1u/u0weeW7xWZBCFChXiyJEj1K1bl8GDB1OqVCkaNGjA1q1bWbhwIQBly5Zl5syZTJkyhVKlSvHjjz8yadKkJPdbp04dVq9eze+//065cuWoV68eBw4cMP18xowZ+Pj4UKtWLdq2bcuQIUMSnDodo0ePHrRo0YJWrVpRtWpVHjx4QO/evc1iunfvTvHixalUqRKenp7s3r073n7y5MnDpk2bOHDgAGXLlqVnz5507dqV0elUrN7Z2ZnNmzfz8OFDKleuzEcffcTbb7/NvJgaF6lozJgxDB48mLFjx1KiRAlatWoVb0p9mzZtyJIlC23atIk3WnbhwoV89NFH9O7dm9dff53u3bsnugr56NGjqVChAg0bNqROnTp4e3vTrFmzeHEdOnQgNDSUKlWq0KdPHwYMGMCnn36a4D4NBgObNm3irbfeonPnzhQrVozWrVvz33//vXTna40aNejZsyetWrXC09OTqS951c7Dw4MffviBTZs2Ubp0aVatWmW2SrgeDFpKqqKmkfnz5zNt2jRu375N2bJlmTt3rtky34n5+eefadOmDU2bNmX9+vXJOlZQUBDu7u4EBgbi5ub2iplbhvBwqFwZTp6EDz6A9evVB0IhkuvoUahSRY1SXL48843MsuR2IT3bR7Dsc5FWNA06d1bvfU9POHJEprxmBtHR0KgR/PMPFCumRipKvbiEpVa78MsvvzBs2DD69etHtWrVADXtaP78+UyePJkScepsPF+Y3hJk1Pbx0SNVc/vWLVUuxxIvqIjMJTISqlVTf4/ffx9+/91yv9vo3S6EhYXh7+9PwYIF43XMCGGprl69SuHChTl48CAVKlTQOx2RgOS2LbovyvLLL78waNAgFi1aRNWqVZk9ezYNGzbk/Pnz5MqVK9HnXb16lSFDhlCrVq10zNYyjR2rOhM9PVUdKEv9gyssU1iYWtE2KkqtqNe+vd4ZiRjSPqaPb79VnYlSPy9zGTtWdSY6O8O6ddKZmB7atGkDwLAEhoK2adPGVCzfYDCkSq0hkTz9+6vOxOLF1bR/IfRmZwcrVkCFCvDnn+pvdKdOemclhHhVkZGRPHjwgNGjR1OtWjXpTMwAdJ/UOHPmTLp3707nzp0pWbIkixYtwtnZme+//z7R50RHR9OuXTu++OILChUqlI7ZWp6dO2HaNLW9eDEk0ccgRIJGj4YzZ8DLS63wLB3SlkPax7S3f7/6Mg1qZUmpn5c5bNgQ23GydCm88Ya++WQW/v7+Sd6uXLli+lekj99+gx9+UBdUli+HFC6wKUSaeeMNmDBBbQ8YANev65uPEOLV7d69m9y5c3Pw4MEX1sMU1kHXEYoREREcPnyYkSNHmh6zsbGhfv367N27N9HnTZgwgVy5ctG1a1d27tyZ5DHCw8MJDw833Q8KCnr1xC3EkydqVWdNU1ftmjbVO6NXFBJiKtrjAtwNDn7pJe9F8mzfDjNnqu0lS9QoV2EZ0qN9hIzdRr7I3buqfl5kpKqfN3So3hklLeTuXVye1W4JuXMHF7mC9FIuXowt6zBwILRurWs6mUr+/Pn1TkHEce8e9OihtocPV/VjrZW0jxnTkCGqlNO+fdC1K2zeLBe+hbBmderUwQIq7olUpGuH4v3794mOjo5X3NLLy4tz584l+Jxdu3axdOnSZC+PPWnSJL744otXTdUiDRoE/v6QP79aiEWIlAgKiu2Q7tZN1agRliM92kfI2G1kUqKioE0buHFD1c/z9ZUvKZlBSIgq7RAUBDVrykq2ejlz5gzXrl0jIiLC7PEPPvhAp4wyn5jV7O/dg9KlYdw4vTMSIj5bWzVytlw52LJFlSjp2VPvrIQQQsTQvYZiSjx58oT27duzePFicubMmaznjBw5kkGDBpnuBwUF4ePjk1Yppps//4xdjc/XV63MK0RKDBwI//0HBQvGjlIU1utl2kfIuG3ki4wZA//+Cy4uqn6etKEZn6bBp5/CqVPg7Q2//qrqdIn0c+XKFZo3b87JkydN9RJBragISN3EdPTzz7B2LWTJojpsHBz0zkiIhBUrpkqSDByoRiy+8w5IRRchhLAMunYo5syZE1tbW+7cuWP2+J07d/D29o4Xf/nyZa5evUqTJk1MjxmNRgCyZMnC+fPnKVy4sNlzHBwccMhgn5Lu3VPD/gE++wzq1NE1HWGFNmyAZctUh/Ty5eDqqndG4nnp0T5CxmwjX+S332DyZLUt9fMyj3nz4Kef1IiXX3+F3Ln1zijzGTBgAAULFmTr1q0ULFiQAwcO8ODBAwYPHsz06dP1Ti/TuHUL+vRR22PGQPny+uYjxIv066f+dm/fDl26qAuCNrqvBCCEEELXptje3p6KFSuydetW02NGo5GtW7dSvXr1ePGvv/46J0+e5NixY6bbBx98QN26dTl27FimGFWjaarezd276kvw11/rnZGwNnfvQvfuanvoUJCFgC2TtI9p48IFNdUf1AWZVq30zUekj927VZkQUAuZSbunj7179zJhwgRy5syJjY0NNjY21KxZk0mTJtE/ZnUkkaZiRuo+egQVK0KcMr1CWCwbG/j+ezWrYPt2mDtX74yEEEKABUx5HjRoEB07dqRSpUpUqVKF2bNnExISQufOnQHo0KEDefLkYdKkSTg6OlKqVCmz52fLlg0g3uMZ1YoV6gqdnR2sXAmOjnpnJKxJzBeJe/egVKnY1fOEZZL2MXUFB6v6eU+eqA6lKVP0zkikh9u3oWVLVTezVSs1bU7oIzo6GtdnQ+Jz5szJrVu3KF68OPnz5+f8+fM6Z5c5LFsGGzeCvb2aoSDT/oW1KFQIpk9XtT9HjIBGjaB4cb2zEkKIzE33weKtWrVi+vTpjB07lnLlynHs2DH++usv00IE165dIyAgQOcsLcPVq2rIP8AXX8gUFZFyy5er6c52dvDDD1IzydJJ+5h6NE2NzD19Wk11lfp5mUNkpOpEDAiAkiVjaw8LfZQqVYrjx48DULVqVaZOncru3buZMGEChVJYFG3Hjh00adKE1157DYPBwPr161/4HD8/PypUqICDgwNFihTB19f3JX4L6/Xff7Ed6l99JeUehPXp0QMaNICwMOjUCaTsqhBC6Ev3DkWAvn378t9//xEeHs7+/fupWrWq6Wd+fn5JfuDz9fVN1odIa2c0qj+cT55AjRowbJjeGaUBW1uiGzXioKcnDRo2xNbWVu+MMpT//oOYGWUTJkDZsvrmI5JH2sfU8c03ahGCLFlUZ2ICZSgtnq29PQc9PTno6Ymtvb3e6ViFESNgxw5VJ3bdOsiaVe+MMrfRo0ebartOmDABf39/atWqxaZNm/jmm29StK+QkBDKli3L/PnzkxXv7+/Pe++9ZyoDMXDgQLp168bmzZtT/HtYI6NR1Z6L+RwZZy2uDEHax8zBYFC1j93cYN8+NWJRZG6+vr6mGTmp4erVqxgMBo4dO2YR+0mO8ePH4+XlleyLa5auU6dONGvWzHS/Tp06DLTy6SXp+X5Ib7pPeRbJM2uWqhni4qKmPWfIvjZHR2z/9z8qA+v1ziWDeb5DeuhQvTMSIv3s3KlWhgSYMQNq1tQ3n5flmC0ble/e1TsNq/Hrr7Er2C9fLlPjLEHDhg1N20WKFOHcuXM8fPiQ7Nmzm1Z6Tq53332Xd999N9nxixYtomDBgsyYMQOAEiVKsGvXLmbNmmWWV0a1YIFayMLJCXx9M97nSGkfMw8fH5g9W3WQjx0L772nyvgI63P79m2+/vprNm7cyM2bN8mVKxflypVj4MCBvP3223qnl2ydOnXi8ePHZp15Pj4+BAQEkDNnzjQ99tmzZ/niiy/47bffqFatGtmzZ0/T44mX8/z7wc/Pj7p16/Lo0aNU7RDXg0WMUBRJO3UKRo1S27NmQQILtQqRpDlzwM8vg3dIC5GAgAD4+GNVP69Nm9iyESJjO3tWfdkEGD4cmjfXNx+hBAYG8vDhQ7PHPDw8ePToEUFBQWl67L1791K/fn2zxxo2bMjevXsTfU54eDhBQUFmN2t08aL6fwAwdSoULapvPkK8qk6d4P33ISJCLbQWGal3RiKlrl69SsWKFfn333+ZNm0aJ0+e5K+//qJu3br0iVmG3orZ2tri7e1NlixpO37r8uXLADRt2hRvb28cEqhnFRERkaY5iBdLr/eDHqRD0cJFRED79urf99+Hbt30zkhYmzNnYldxnDFDOqRF5hEZqToTb99WoxcWL5b6eZnBkydq8Z2QEKhXT9WKE5ahdevW/Pzzz/Ee//XXX2ndunWaHvv27dum+rMxvLy8CAoKIjQ0NMHnTJo0CXd3d9PNx8cnTXNMC9HRqvPl6VP1/6F3b70zEuLVGQzw3XeQPTscOQKTJumdkUip3r17YzAYOHDgAB9++CHFihXjjTfeYNCgQezbt88UN3PmTEqXLo2Liws+Pj707t2b4ODgJPf9xx9/ULlyZRwdHcmZMyfN41xVTGhacLZs2RItIRQdHU3Xrl0pWLAgTk5OFC9enDlz5ph+Pn78eJYvX86GDRswGAwYDAb8/PwSnOK6fft2qlSpgoODA7lz52bEiBFERUWZfl6nTh369+/PsGHD8PDwwNvbm/Hjxyf6e44fP54mTZoAYGNjYxrpHzNl+Ouvv+a1116j+LMpGidPnqRevXo4OTmRI0cOPv30U7NzGfO8iRMn4uXlRbZs2ZgwYQJRUVEMHToUDw8P8ubNy7Jly5I8/0ajkalTp1KkSBEcHBzIly8fX3/9tenn169f5+OPPyZbtmx4eHjQtGlTrl69muQ+XySp13zlypVUqlQJV1dXvL29adu2LXfjjGj38/PDYDCwceNGypQpg6OjI9WqVePUqVOmmAcPHtCmTRvy5MmDs7MzpUuXZtWqVcn+veO+H65evUrdunUBTDM0OnXqxIoVK8iRIwfh4eFm+23WrBnt27d/pfOTlqRD0cKNGwfHjkGOHJngy3BICJqLCyEGA57OzoSEhOidkdWLiIBPPoHwcHj3XbXCsxCZxbBhsGuXqrW0dq0aoWvNQu7eJcRgUDeZ2pcgTYPOneHcOcibF1atUnUzhWXYv3+/6UN0XHXq1GH//v06ZJS0kSNHEhgYaLpdv35d75RSbOZM2LNH1RH9/nuwyaCf/KV9zHxy54Z589T2l1/C0aP65mORQkISv4WFJT/2+YsuCcWkwMOHD/nrr7/o06cPLgl8OIs7BdTGxoZvvvmG06dPs3z5cv7991+GJbGYwMaNG2nevDmNGzfm6NGjbN26lSpVqqQov7iMRiN58+Zl9erVnDlzhrFjxzJq1Ch+/fVXAIYMGcLHH39Mo0aNCAgIICAggBo1asTbz82bN2ncuDGVK1fm+PHjLFy4kKVLl/LVc1c9ly9fjouLC/v372fq1KlMmDCBLVu2JJjbkCFDTJ17MceOsXXrVs6fP8+WLVv4888/CQkJoWHDhmTPnp2DBw+yevVq/vnnH/r27Wu2z3///Zdbt26xY8cOZs6cybhx43j//ffJnj07+/fvp2fPnvTo0YMbN24kes5GjhzJ5MmTGTNmDGfOnOGnn34yXdCLjIykYcOGuLq6snPnTnbv3k3WrFlp1KjRS4+kfNFrHhkZyZdffsnx48dZv349V69epVOnTvH2M3ToUGbMmMHBgwfx9PSkSZMmRD4b/hwWFkbFihXZuHEjp06d4tNPP6V9+/YcOHAgWb93XD4+PqxduxaA8+fPExAQwJw5c2jZsiXR0dH8/vvvpti7d++yceNGusRMu7FEWiYTGBioAVpgYKDeqbzQzp2aZjBoGmja2rV6Z5MOgoPVLwuaM2jBwcF6Z2T1Ro9Wp9TDQ9Nu3dI7G8tlTe1CWsso52LVKlNzoq1fr3c2qSP4zh3TLxV8547e6VikadPUKbKz07R9+/TOJuNIrXbB2dlZO3HiRLzHT5w4oTk5Ob30fgHtt99+SzKmVq1a2oABA8we+/777zU3N7dkH8fa2sdTpzTN3l79n1i6VO9s0pa0j5mT0ahpLVqol750aU0LC0v/HPRuF0JDQ7UzZ85ooaGh8X8Y80EooVvjxuaxzs6Jx9aubR6bM2f8mBTYv3+/Bmjr1q1L2S+radrq1au1HDlymO4vW7ZMc3d3N92vXr261q5du0Sfn9DfC3d3d23ZsmWapmmav7+/BmhHjx5NdB99+vTRPvzwQ9P9jh07ak2bNjWLeX4/o0aN0ooXL64ZjUZTzPz587WsWbNq0dHRmqZpWu3atbWaNWua7ady5cra8OHDE83lt99+057v0unYsaPm5eWlhYeHmx777rvvtOzZs5t9v964caNmY2Oj3b592/S8/Pnzm/LRNE0rXry4VqtWLdP9qKgozcXFRVu1alWC+QQFBWkODg7a4sWLE/z5ypUr452H8PBwzcnJSdu8ebMpj7jns3bt2vH+fsf1otf8eQcPHtQA7cmTJ5qmadq2bds0QPv5559NMQ8ePNCcnJy0X375JdH9vPfee9rgwYM1TXvx7/38+yHmmI8ePTKL69Wrl/buu++a7s+YMUMrVKiQ2flKL0m2LXFk0OuU1i8oSE111jQ1VaVFC70zEtZm3z6YOFFtL1qkruQKkRmcPg1du6rtkSOhaVN98xHpY9u22Dpx33wDcRZEFxaiSpUqfPfdd/EeX7RoERUrVkzTY1evXp2tW7eaPbZlyxaqV6+epsfVS2QkdOigZiq8954auStERmMwwMKF4OkJJ0/CF1/onZFIDk3Tkh37zz//8Pbbb5MnTx5cXV1p3749Dx484OnTpwnGHzt2LNUXdJk/fz4VK1bE09OTrFmz8t1333Ht2rUU7ePs2bNUr17dbAGyN998k+DgYLPRfmXKlDF7Xu7cuc2m5yZX6dKlsY+z4v3Zs2cpW7as2YjQN998E6PRyPnz502PvfHGG9jEGcru5eVF6dKlTfdtbW3JkSNHojmdPXuW8PDwRF+D48ePc+nSJVxdXcmaNStZs2bFw8ODsLAwUz3IlHrRa3748GGaNGlCvnz5cHV1pXbt2gDxXsO4nwc8PDwoXrw4Z8+eBdTU9y+//JLSpUvj4eFB1qxZ2bx5s2kfL/q9k6t79+78/fff3Lx5E1CrmHfq1CnFC9elJ5kIZKE++wyuXoUCBdSCGkKkREiI+iJhNELbttCypd4ZCZE+AgPVBZinT+Htt9U0KJHx3bgBrVqpNq9jR+jRQ++MREK++uor6tevz/Hjx00furdu3crBgwf5+++/U7Sv4OBgLl26ZLrv7+/PsWPH8PDwIF++fIwcOZKbN2+yYsUKAHr27Mm8efMYNmwYXbp04d9//+XXX39l48aNqfcLWpCvvlK15Tw8MkHJHJGp5cqlLpx/+CFMmaIuIsoFpWeSqjX4/AqNSXVaPV8r4RXr3RUtWhSDwcC5c+eSjLt69Srvv/8+vXr14uuvv8bDw4Ndu3bRtWtXIiIicHZ2jvccJyenJPdpMBjidWhGJrGqz88//8yQIUOYMWMG1atXx9XVlWnTpqVZmQ47O7t4+RqNxhTvJ6Gp5C97/JTk9KLzHxwcTMWKFfnxxx/j/czT0zOF2b74mDFTvRs2bMiPP/6Ip6cn165do2HDhimaYj1t2jTmzJnD7NmzTTU9Bw4caNrHi37v5Cpfvjxly5ZlxYoVvPPOO5w+fdriP6fICEULtH69qnNjMKgVed3c9M5IWJvhw9WqjnnyxNaXESKjixnRfeEC+Pio+nmyonnGFxGhLprcuwdly8KCBdJ5YqnefPNN9u7di4+PD7/++it//PEHRYoU4cSJE9SqVStF+zp06BDly5enfPnyAAwaNIjy5cszduxYQNWTijv6oGDBgmzcuJEtW7ZQtmxZZsyYwZIlS2jYsGHq/YIW4uBBiKl/v2CBzFAQGV+LFuoCesxFpUTWWcp8XFwSvzk6Jj/2+c6ShGJSwMPDg4YNGzJ//vwEa+Y/fvwYUCPLjEYjM2bMoFq1ahQrVoxbt24lue8yZcrEG40el6enp1mtwYsXLyY62hFg9+7d1KhRg969e1O+fHmKFCkSbySdvb090dHRSeZVokQJ9u7da9aZuXv3blxdXcmbN2+Sz00NJUqU4Pjx42bne/fu3djY2JgWbUkNRYsWxcnJKdHXoEKFCly8eJFcuXJRpEgRs5u7u/tLHTOp1/zcuXM8ePCAyZMnU6tWLV5//fVER1fGXQzo0aNHXLhwgRIlSgDqXDVt2pRPPvmEsmXLUqhQIS5cuJDs3/t5MaNHE3rfdOvWDV9fX5YtW0b9+vUtfkE46VC0MHfuQPfuanvYMEjh52sh+PtvmD9fbS9bplbAEyIzmDpVXZCxt4c1a9QUKJHxDR6sSjxky6YW30lgwIKwIOXKlePHH3/k9OnTHDp0iO+//56iRYumeD916tRB07R4t5iVOn19ffHz84v3nKNHjxIeHs7ly5cTLMpu7UJDVYdKdLQatduqld4ZCZE+5s5Vnefnz8Po0XpnI15k/vz5REdHU6VKFdauXcvFixc5e/Ys33zzjWnqaZEiRYiMjGTu3LlcuXKFlStXsmjRoiT3O27cOFatWsW4ceM4e/YsJ0+eZMqUKaaf16tXj3nz5nH06FEOHTpEz549443Ai6to0aIcOnSIzZs3c+HCBcaMGcPBgwfNYgoUKMCJEyc4f/489+/fT3DEY+/evbl+/Tr9+vXj3LlzbNiwgXHjxjFo0CCzKcZppV27djg6OtKxY0dOnTrFtm3b6NevH+3bt09w4ZCX5ejoyPDhwxk2bBgrVqzg8uXL7Nu3j6VLl5ryyJkzJ02bNmXnzp34+/vj5+dH//79k1zoJSlJveb58uXD3t7e9B76/fff+TKR6UsTJkxg69atnDp1ik6dOpEzZ06aNWsGqPfBli1b2LNnD2fPnqVHjx7cuXMn2b/38/Lnz4/BYODPP//k3r17Zqttt23blhs3brB48WLLXozlGelQtCCaBt26wf37apSF1AERKfXoUWydpL59oUEDffMRIr1s3QqjRqntuXPhFRb0E1bkhx9iR2H/8AMULqxvPkLobfRoOHsWvL1jLy4KkRnETO8HmDULdu7UNx+RtEKFCnHkyBHq1q3L4MGDKVWqFA0aNGDr1q0sXLgQgLJlyzJz5kymTJlCqVKl+PHHH5k0aVKS+61Tpw6rV6/m999/p1y5ctSrV89sJd4ZM2bg4+NDrVq1aNu2LUOGDElw6nSMHj160KJFC1q1akXVqlV58OABvXv3Novp3r07xYsXp1KlSnh6erJ79+54+8mTJw+bNm3iwIEDlC1blp49e9K1a1dGp1Pvt7OzM5s3b+bhw4dUrlyZjz76iLfffpt5aTCVbcyYMQwePJixY8dSokQJWrVqZRoV6OzszI4dO8iXLx8tWrSgRIkSdO3albCwMNxeclpmUq+5p6cnvr6+rF69mpIlSzJ58mSmT5+e4H4mT57MgAEDqFixIrdv3+aPP/4wjSQcPXo0FSpUoGHDhtSpUwdvb29TZ2Nyfu/n5cmThy+++IIRI0bg5eVlttq2u7s7H374IVmzZo13DEtk0FJSFTUDCAoKwt3dncDAwJd+06aV775TdZ/s7eHwYShVSu+M0lloKNENG3LyxAmGlS7Nhr//TrV6BJlF27ZqmmexYnD0qIzUSS5LbhfSmzWei+vXoUIFdTGmc2dYujRjTnkNffiQ84UKAVD8yhWcPDx0zkhfJ05AtWpqRNaYMTBhgt4ZZVzW2C6kBUs/D9u3Q9266gL1n3+qxVgyC2kfRYwuXdQMncKF4fjxFM/GTTG924WwsDD8/f0pWLAgjs9PYxZCvJCfnx9169bl0aNHZMuWTe90AHj77bd54403+Oabb3TLIbltiyzKYiEuXlQLsQBMmpQJOxMBnJyw3bGDckDKSrMLgF9+ia0Zt2KFdCaKzCE8HD76SHUmli+vRuRkxM5EACcPD8o9qyuU2T1+rGpmhYZCo0YwbpzeGQmhrydPVA1ZTVOr3GemzkSQ9lHEmjUL/vkHLl9WNcWllrgQwlo8evQIPz8//Pz8WLBggd7pJItMebYAUVHQvr1albRuXRg4UO+MhLW5dQt69VLbo0bJ6nYi8xg4EA4cULVC166NXzdcZDxGo1rF/vJlKFAAfvxRFt8RYsgQtfBq/vwwc6be2QihH3d3tbglqIuMyVwjQQghdFe+fHk6derElClTUnWxnLQkIxQtwMSJsH+/+gO4fDmkQ11WkYHEjEZ49EhN+xwzRu+MhEgfvr6waJEakfjjj1CwoN4ZifQwaRL88Qc4OKhOZJnZKDK7TZtU2RxQ7aIFzsYWIl3Vr68utC9cqKZAnzwp/y+EEAmLWeTNEly9elXvFFJMOhR1duBAbN2nBQvAwlcFT1shIWj58/Pg4UMqenhw5r//cEnrwicZwLffwl9/qS/XK1dCEguVCZFhHD0aOyp3/Hh4911d00kXIXfvEurtDYDT7du45Mqlc0bp7++/Yy+aLFigLqIIy9aiRYtkx65bty4NM8mYHjxQC/qBGrFdp46e2ehH2kfxvKlTYfNmuHIFBg+OXbBFCCFE6pEORR2FhMAnn0B0NLRurRbUyOwMDx6QE7j/4IHeqViFS5fUhySAyZOhZEl98xEiPTx8CB9+CGFh0LixWtU0s8j57ApqiM556OHqVWjTRo3K7t5djToRls/d3V3vFDK0Pn0gIABef13NeMnMMnP7KOLLmlUtzlKnDixZouruZoaLj0IIkZ6kQ1FHQ4aoxVjy5lUjLYRIiagoVUcspvZm//56ZyRE2jMa1YUYf38oVAj+396dhzdVpX8A/6bpRveW0hZKKchSBARkq2XVAUVUGBAdhmFkEREVEKYgi8oqmwKCDIiI0AIq4LD+BhWGQVbZqVXWkVUWu1BLW5quJPf3x6FJC02alCYny/fzPPfhJjm5901p3yTnnvOeL79kmQhXUFAgFt/JzATatAEkLnpHFkpISJAdgtNav14syKZWixkKrCFLVFbnzsDo0cCiRWIk7+nTouayM7KXKZtE5BzMzSn8GibJd9+J2l+AqHfjrG9uZD3z5gGHD4uaMImJ7FQh1/DBB8D33wPe3qJ+HnOnaxg1Cjh5EqheHdi4Ufz/E7my338H3npL7L//vuhoJ6IHzZ4NNGok/mac8eK7x71aR3l5eZIjISJnUpJTPCqop8YRihLcumWYqjVmDNC1q9RwyAElJwNTp4r9f/4TqFNHajhENvHdd8D06WJ/+XKgZUup4ZCNfPGF2FQqYN06sYotOY7HH38cKpXKrLZJSUlWjsY5KIoYbXX7NtC6NfDee7IjIrJf1aqJRS87dBCzGvr2BXr3lh1V1VGr1QgKCkJ6ejoAwMfHx+ycS0R0P0VRkJeXh/T0dAQFBUGtVptszw5FG1MU4PXXgbQ0oGlTsVolkSUKCoBXXgGKi4E+fcQ+kbO7ckVMdVYUsRjLwIGyIyJbOHECGDlS7M+cCTz9tNx4yHK9nembu51YsUKM1PbyAtas4WJsRBV54glg/HhRb3z4cKBjRyA0VHZUVSfi3oJEJZ2KREQPKygoSJ9bTGGHoo0lJgJbt4oPf19+yWlbZLnJk0UNmPBwMUqLFyHJ2eXni2Lqt28DsbHAwoWyIyJb+OMPUTexsBDo1QuYOFF2RFQZU0uG01OVuHwZiI8X+7NnczE2InNNmwZs3y4+Q7/5JvDNN87zGVqlUqFmzZoICwtDcXGx7HCIyMF5eHhUODKxBDsUbejKFUPtjg8+4HS9B7i5QdeqFc6fP4/HGzeGG4sCPmD/fmDBArG/YgVQo4bceIisrWREYnKy+H3fuFGMynFFbu7uOOvjAwCo5+7cb99aLTBgAPDbb0CDBmK6Gt8SnENWVhY2btyIS5cu4Z133kFISAiSkpIQHh6OyMhI2eHZNa0WGDQI0GjEYhNjxsiOyH64Un6kyvHyEu8lsbHis8SGDcBf/yo7qqqlVqvN7gQgIqoKfMe1Ea1WTNHLzQU6dRIrPNN9qlWD28mTaALgoOxY7FBOjvgioSjA0KFAz56yIyKyvs8/N3QmrV8P1K4tOyJ5qoWEoIlGIzsMm5g+Hdi5U9S+2rwZCAqSHRFVhV9++QXdunVDYGAgrl69imHDhiEkJASbN2/GtWvXsGbNGtkh2rWFC4GDBwE/Py7Gdj9Xyo9Uea1aiZqj06cDI0YATz4JmDGjj4iIjOBHERuZN098CPT3F/VuePGILBUfD1y9CtStC3z8sexoiKzv6FGxui8g6s3+6U9y4yHb2L5djOIHxEjsxx6TGw9Vnfj4eAwePBgXLlyAd6maL8899xz2798vMTL7d+aMYfGVjz8G6tWTGw+Ro3rvPeDxx4HMTFHXXlFkR0RE5LjYoWgDP/0ETJki9hcvFh1CRJb4v/8DVq4UtV5WrwYCAmRHRGRdt26J+nkliw+9847siMgWLl4Ui+8AojN5wAC58VDVOn78OIYPH/7A/ZGRkUhNTZUQkWMoLhazXIqKgB49xArPRFQ5Hh7is7SHB/Dvf4uBHkREVDnsULSyggLx5ajkS/GgQbIjsmN5edBFR+OGuzsa16mDvLw82RHZhVu3gGHDxP7YsaJuEpEzu3tX1DW6cQNo1EhM7XOWwukPIy8jAzfc3XHD3R15GRmyw6lyeXlA375AdjbQvj0wf77siKiqeXl5IScn54H7f/31V9RgUWCjZs0CkpKA4GDgiy+YD8vj7PmRqtZjjwEzZoj9t98Grl+XGw8RkaOyiw7FpUuXom7duvD29kZsbCyOHTtmtO2KFSvQqVMnBAcHIzg4GN26dTPZXrZJk4CzZ7kir1kUBW7XrqG2Vovr169D4RwEKAowfDiQng40a2aYBkiuw5nzozGTJwM//AD4+or6eRyRKyg6HWprtait1ULR6WSHU6UUBXjjDeCXX4CwMLH6pqen7KioqvXq1QszZszQr0KqUqlw7do1TJgwAX379pUcnX06cQKYOVPsf/opUKuW3HjslTPnR7KOcePEAi05OWLUL792EBFZTnqH4oYNGxAfH4+pU6ciKSkJLVq0QPfu3ZGenl5u+71796J///7Ys2cPDh8+jKioKDzzzDO4efOmjSOv2O7dwKJFYn/lSq7IS5ZbuxbYskVMy1i7FihVcopcgDPnR2O2bAHmzhX7K1cCTZvKjYds47PPRI5Tq8XKm1zs1zktWLAAubm5CAsLQ35+Prp06YIGDRrA398fs2bNkh2e3cnPF1OdtVrgL39xvhVpiWRydxdTn729gf/8RywCR0REllEpkoeBxcbGom3btliyZAkAQKfTISoqCqNGjcLEiRMrfL5Wq0VwcDCWLFmCgQMHVtg+JycHgYGByM7ORoAVh73cvg00by6m7A0fLr4sUQU0GrF0IQBfAOm5ufD19ZUbk0TXrokpGTk5wOzZYrQrWYet8oKlbJ0fAbk/i19/Bdq0Ae7cAcaMESuakoEmPR2+4eFiPy0NvmFhkiOqGkeOiFIOxcVimvPYsbIjovtVdV748ccf8fPPPyM3NxetWrVCt27dqiBK67N1fhw7VizAEhEBnD4NVK9u9VM6LGfNj2R9CxeKhQ99fYFTpyxf8MheP0MSEdmCu8yTFxUV4eTJk5hUqqfEzc0N3bp1w+HDh806Rl5eHoqLixESElLu44WFhSgsLNTfLq92jzWMHCk6Exs0YB0ospxOBwweLDoT4+K4IIUrskV+BOTlyPvl5gIvvig6Ezt1Aj76SEoYZGPp6YbFd156SXypI+fXoUMHdOjQQXYYdm3/fsNFlS++YGcikbWMHi1mRxw4AAwZIkquuEmfw0dE5BikpsuMjAxotVqE37uiWCI8PNzs1f4mTJiAWrVqGb26PWfOHAQGBuq3qKioh467Ihs2AF9/Ld6M1q7VD7ojMts//wns2QP4+IjV59yldv2TDLbIj4CcHHk/RRH1i86cAWrWFPXzPDxsHgbZWMniOzdvAo0bA6tWsc6ws/rhhx/QpEmTci9YZGdno2nTpjhw4IDFx7WkxmxiYiJUKlWZzdtO64jcuSMuKioK8OqrwPPPy46IyHm5uQEJCeIz9759wL1JIUREZAaHvv4yd+5crF+/Hlu2bDH6oXDSpEnIzs7Wb9etvIzXzZvAm2+K/ffeA554wqqnIyd07hxQMpt1wQIxypXIUubkR8D2ObI8ixeLCzHu7qIzMSLC5iGQBO+9Jy6c+PmJxXf8/WVHRNayaNEiDBs2rNzpgIGBgRg+fDg+/vhji45paY1ZAAgICEBKSop+++233yx+LbYwbhxw5QoQHc3SD0S2UL8+MG+e2J84UZRgISKiikntUAwNDYVarUZaWlqZ+9PS0hBRwTfK+fPnY+7cufjPf/6D5s2bG23n5eWFgICAMpu16HRiqPzt20Dr1mKlUrKASgXdo4/iopcXGjduDJULDlUpLgZeeQUoKACefVbU3yTXZIv8CNg2R5bnwAHx5RkQHegdO9r09A5F5eaGi15euOjlBZWDz8fatMkwrX3VKuDRR+XGQ9b1888/49lnnzX6+DPPPIOTJ09adMyPP/4Yw4YNw5AhQ9CkSRN89tln8PHxwapVq4w+R6VSISIiQr/dPwLcHuzYYVgcIiGBq9yby5nyI8nxxhtA165iMaTBg8ViSEREZJrUd1xPT0+0bt0au3fv1t+n0+mwe/duxMXFGX3eRx99hA8++AA7duxAmzZtbBGqWT79FNi1S6wW9uWXnLJnMR8fuJ09iwYFBTh57hx8fHxkR2RzM2cCJ08CISFihVsX7FOle5wtP5YnJUWsXHr3LtC/PzBqlOyI7JtPaCgaFBSgQUEBfEJDZYdTaefPi4tvgFh04uWX5cZD1peWlgYPEx+K3N3dcevWLbOPV1JjtnQ5B3NqzObm5iI6OhpRUVH485//jDNnzph9Tlu4fRsYOlTsv/028NRTcuNxJM6SH0keNzdxgcvfHzh8WCyIREREpkm/hBcfH48VK1Zg9erVOHfuHN58801oNBoMufdtY+DAgWUWJfjwww8xefJkrFq1CnXr1kVqaipSU1ORm5sr6yUAEF+QShbOmDdP1IMissSxY8CsWWJ/2TKgVi258ZB8zpIfy1NcLDoTU1OBZs2AFSvYge4KSi++06ULMHeu7IjIFiIjI3H69Gmjj//yyy+oWbOm2cerTI3ZmJgYrFq1Ctu2bcOXX34JnU6H9u3b48aNG0bPU1hYiJycnDKbNY0aBfz+O9CoETBnjlVPRUTlqFMHWLRI7E+eDJw9KzUcIiK7J71DsV+/fpg/fz6mTJmCli1bIjk5GTt27NB/SLx27RpSUlL07ZctW4aioiK89NJLqFmzpn6bL3Ep5aIiYMAAMU316aeBt96SFgo5qLw8MdVZqxUjtf7yF9kRkT1whvxozPjxwMGDYjrfpk2Ar6/siMjaFEWMvjp3TlwwKambSc7vueeew+TJk1FQUPDAY/n5+Zg6dSpeeOEFq8YQFxeHgQMHomXLlujSpQs2b96MGjVqYPny5UafY8tFqzZtAr76SoySWrNGLBBBRLY3ZIhYCKmwEBg0SFwAJSKi8qkURVFkB2FLOTk5CAwMRHZ2dpXVCps4EfjwQzFN9dQpjiyrtLw86Nq0weXLl9GvXj0cOHnSZaY9jxolVpWrVQs4fRoIDpYdkWuxRl5wVLb4WaxfLzrOAWDrVuDPf7bKaZxOXkYGfq9dGwBQ68YNh5vWt3AhEB8vOhH37QPat5cdEZnrYfNCWloaWrVqBbVajZEjRyImJgYAcP78eSxduhRarRZJSUlm1zQsKiqCj48PNm7ciN69e+vvHzRoELKysrBt2zazjvPyyy/D3d0d69atK/fxwsJCFBYW6m/n5OQgKiqqyvNjWpoYqZ2RAbz7rmG2ApnP0fMj2Zfffxd/k7dvAzNmmK6Lz8+QROTKpI9QdHR79xoKy3/xBTsTH4qiwO3cOTQoLMT58+fhKn3du3aJzkRAFGBnZyI5szNnDDXCJk1iZ6IlFJ0ODQoL0aCwEIpOJzsci+zfbygLsnAhOxNdTXh4OA4dOoRmzZph0qRJ6NOnD/r06YN3330XzZo1w8GDBy1aIKWyNWZL02q1OHXqlMmp1rZYtEpRxAJsGRlA8+bA1KlVfgqX4Mj5kexPrVrAP/8p9mfMAJKTpYZDRGS3ONnoIdy+DQwcaJjG1aeP7IjI0dy+bVicYMQI4Jln5MZDZE3Z2aJ+Xl4e0K0b8MEHsiMiW/j9d1HGQasF/vY3kevI9URHR+O7777D7du3cfHiRSiKgoYNGyK4klfR4uPjMWjQILRp0wbt2rXDokWLHqgxGxkZiTn3ihHOmDEDTzzxBBo0aICsrCzMmzcPv/32G1577bUqe42VkZAAbNsmFvJbswbw9JQaDhHd87e/iVIEW7aIqc/Hj/Pvk4jofuxQrCRFAd58E7h+HWjQwFDAl8gSo0YBN28CDRuKafNEzkpRROf5r78CUVHA118DarXsqMjaShbfKZnS+fnnXHzH1QUHB6Nt27YPfZx+/frh1q1bmDJlClJTU9GyZcsHasy6uRkm4ty+fRvDhg1DamoqgoOD0bp1axw6dAhNmjR56Fgq6/JlYPRosT9zJtCihbRQiOg+KhXw2WfAgQPAL7+IkYozZ8qOiojIvrCGYiWtXStGJ6rVwI8/ArGxVRikq9JoAD8/AIAvgPTcXPg68UoN//qX+KLt5gYcOsTfIZlY/8bAWj+Ljz4CJkwQV/cPHADatauyQ7sMTXo6fO91lmjS0uAbFiY5ooqNGQN88olYfOfECXHxhBwPc6RQlT8HrVasdP7jj0CnTsCePbzI8jAcMT+SY9i0CXjpJfF5/fDhBz+/MD8SkStjDcVKuHLFMGVr2jR2BJHlUlKAN94Q++++y98hcm67d4t6iYCoScTORNewbp3oTATEVE52JhIZzJsnOhP9/cXfBzsTiexT375iITmdTkx9zs+XHRERkf1gh6KFtFrglVeAO3eADh0MX5KJzFVSczMzE2jVyvTKcUSO7vp14K9/FR/EBw8Ghg2THRHZwunTQElpunff5eI7RKUlJwNTpoj9xYuBunVlRkNEFVmyBIiIAM6f5+d2IqLS2KFoodJXlNeu5RXlKqVSQVenDm6o1YiKioLKSQttff458P33gJcXC7CTcyssFNOEMjKAxx8HPv2U9fMehsrNDTfUatxQq6Fys9+37/sX35kxQ3ZERPajoAD4+99FfdHevcWIJ3p4jpIfyTGFhAArVoj9jz8GDh6UGw8Rkb3goiwWKH1F+Z//BOrVkxqO8/Hxgdtvv6E2gPOyY7GSixeB+HixP2cO0LSp3HiIrGnMGODYMSA4WNQgqlZNdkSOzSc0FD5378oOw6SSKWEXLgB16ohpz7zwRmTw3nvAmTNAWBgXKapKjpAfybG98IKYaZGYKP79+WfAiUu9ExGZhZfwzFT6inKfPmJBFiJLaLXii3ZeHvDkk4aVHYmcUWKiWB1RpQK++ooXYFzFRx8B27aJkdcbNwKhobIjIrIfe/aI0U0AsHIlUKOG3HiIyDKLFgFRUcClS8DEibKjISKSjx2KZnr/fXFFOTwcWL6cV5TJch99JFZzDggQnS2ckUPO6qefgDffFPtTpwI9esiNh2zjv/8Vo68AMYq/bVu58RDZk5wcYMgQsT9smBjtRESOJTBQXAwARF3FH36QGw8RkWzs0jDD3r2GK8pffMErylaTnw9d69Y46+uLjq1bI9+JllFLThYdK4AowB4dLTUcIqvJzBQrIhYUAM89x+LlVSk/MxNnfX1x1tcX+ZmZssMp49o1wyqYr77KxXeI7jd2LPDbb2IBlgULZEfjfOw5P5Jzefpp4I03xP6QIeJiARGRq2INxQpkZ4tpqooiVqzkFWUr0unglpSEJgB+SkqCTqeTHVGVKCgQK4OXFGDndHlyVjqdKA1x5YqY4rx2LUfiViXd3btokpcHANDYUa2w0ovvtGolRm1wFD+RwfffiwvSgJih4O8vNRynZK/5kZzTvHnAzp3i807JyHwiIlfEr3oVGD1ajLyoV88wSpHIElOmAKdPswA7Ob8PPhBfnL29xSIsISGyIyJbGD0aOH5c/H9z8R2isjIzgaFDxf6YMUCXLlLDIaIq4OcHJCSI/TVr5MZCRCQTOxRN2LIFWL1ajLBZu5ZXlMly+/cD8+eL/RUrOF2enNd33wHTp4v9zz4DHn9cbjxkGwkJhrrCX30lpnMSkcHbbwMpKUBMDDB7tuxoiKiqdOkiLhIQEbkydigakZoKvP662B8/HujQQW485HiuXBHTABVF1BTr1Ut2RETWcfkyMGCA+F1/4w1RJoKcX1KSYfGdadOAZ5+VGg6R3dm0SXS0u7mJC9QcvUvkXGbPBurXlx0FEZE87FAsh6KIzsSMDKBFC8OoGyJzZWUBzz8P3LolRmp98onsiIisIz9fLMKSlQW0awcsWiQ7IrKFksV3CgvF4jvvvy87IiL7kp5uWLhh4kQgNlZuPERU9apVAzZulB0FEZE87FAsx6pVwL//DXh6iqnOnp6yIyJHUlwMvPwycO4cEBkpfpf8/GRHRVT1FEWMUEtOBkJDxYdqLy/ZUZG16XRiROrVq6K+8JdfcvEdotJKRmtnZADNm4taykTknB55RHYERETy8CvAfa5cMdTDmDkTeOwxqeG4HKV6dWSoVAitXl12KJWiKMCIEcB//wv4+orOxMhI2VERWcfnnxvqzG7YAERFyY7I+WWoVMiQvLLTjBnAjh1i8Z3Nm4HgYKnhENmdr74Sdbjd3UWO5IUW27CH/EhERORK3GUHYE+0WlH7KzcX6NQJiI+XHZGL8fWFKiMDoQB+kx1LJc2fLxZfcXMD1q3jwhTkvI4eFYsNAMCcOcCf/iQ3HlfgGxYGX51OagzffmsoA7J8OdCypdRwiOzOjRvAyJFif9o0/o3Yij3kRyIiIlfDEYqlLFwIHDggpqcmJgJqteyIyJFs3gxMmCD2P/4Y6NlTbjxE1pKWJurnFRUBffoA77wjOyKyhcuXgb//Xey/+SYwcKDceIjsjaIAQ4cC2dmipmzJZwIiIiIiZ8QOxXtOnwbee0/sL1zIehhkPkUBli0zrHI7YoRh5BaRs7l7F/jrX4GbN4GYGHHxhTPMnF/pxXdiY8X7JBGVtXw58J//iHIAq1eLKc9EREREzoodihCjbF55Rfz7wgvi6jJJkJ8PbefOSA4KwjOdOiE/P192RBW6fh3o3h146y2goADo1UuscssOFnJWEycCe/eKkdxbtgABAbIjch35mZlIDgpCclAQ8jMzbXbe0ovv1KjBxXeIynPpEjBunNifOxdo3FhuPK5GVn4kIiJyZbx2ClEPKjkZqF5d1L9jZ5AkOh3UBw6gJYAfDx6Ezo5r4SgKsGaNGImYkyNGI3z4oaibxNVOyVlt2AAsWCD2V68GHn1UbjyuRnf3LlpmZwMANHfv2uy8y5YZFt9Zvx6oXdtmpyZyCCU1uDUa4MkngVGjZEfkemTlRyIiIlfm8h2Khw6JK8mAmKoSESE3HrJ/ly+LBXu2bRO3Y2PFl+2YGLlxEVnT6dPAq6+K/QkTgBdflBsP2cauXVx8h6giCxcCP/4I+PsDCQm8sEhERESuwaU7FHNzRVF5nU5Mee7bV3ZEZI8KCoD9+4Hvvxfb//4n7vfwEKNb33mHdZLIuWVliQ7EvDygWzdg5kzZEZEtnDsHvPyyGH31yitcfIeoPMnJZWtw160rMxoiIiIi23HZbhDNrVuY/IE7Ll3yQe1ILeZMzoQmXYHa0xPeQUGGdunpRo/h5u6OaiEhlWqbl5EBxciUXpWbG3xCQyvVNj8zEzoTUz18w8Iq1bYgKwvaoqIqaesTGgrVvcv3hTk5uFtQIB7QaOBbqp0mPR0+0dHlty1HtZAQuN3r2SvKzUVxXp7Rth4BISi66478fCAnIxd52XnIyhKr15bebtwA9h4JQl6hp3gecuGnykPHjqIzsWlToDATKLx3XO+gIKg9RdvivDwU5eYajcErIADu3t4Wt71bUIDCnByjbT39/ODh42NxW21REQqysoy29fDxgaefn8VtdXfvmqxnZElbd29veN0r2qfodMjLyKiStgUmfq9cXWYm8NJLwIULQJ06wLp17EB3BbduAc8/L1ar7dCB5UCIyvPjj6L2dlGR+HspGcVNRERE5BIUO7BkyRIlOjpa8fLyUtq1a6ccPXrUZPtvvvlGiYmJUby8vJRmzZop3377rdnnys7OVgAoG9FNEZXwFOW/+JNScmOnew2lXTtFadVKUVq0UJTckkblbPvcApWmTRX9lg6V0bYnVD7Ko48q+u0K1EbbnlF5KTExin47o/Iy2vYK1ErDhop+O67yMdo2HSqlfn1Fv+1TBRptmwso9eop+u17txpG2yqAUreuot82qSNNtm1YK02pU0dR6tRRlLXu9ctt43MvhqZhZ5XISEWJjFSU5e7NTB738eADSliYotSooSgfu7cx2bYJtupvTkUXk23bIFGJjFSUoUMVZV2r50y2/WnhQv3v2t6XXzbZ9tjUqfq2B4YONdn20D/+oW976B//MNn2wNCh+rbHpk412Xbvyy/r2/60cKHJtnuee07f9kxioum2Xbro217YutV02zZt9G2vHzhgOt5mzfRtb509a/rnUL++vm1uWprJtrtq1lQAKNnZ2WbnEluxZX5UFEOOzM7OVk6eFH/TgKL4+CjKiRMP80roYZX+Pc5NS7PaeQoKFKVjR3GqevUUJT3daqciB1E6L9gTmflx2zZF8fYWfydxcYryxx8P80roYdkqPxLdz17zIxGRLUiv8rJhwwbEx8dj6tSpSEpKQosWLdC9e3ekGxntd+jQIfTv3x9Dhw7FTz/9hN69e6N37944ffq0RecdgSUAgNFYhK74QX9/8V3g2DEgKQn4+WfTx9DpgDNnDJvJtoqYPlaymaIoYlptyaYopttfuGDYKmp76ZJh01XQ9soVw6atYH2Uq1cNm1Zruu3N34Fr18RWUd3stHTg5k2xFVfQNvM2kJ4uRtZYUo+7okE3Mz8Qqzl/8QVrbJJtycqPAPDVV2Jk2tWrQP36wOHDQOvWD/mCyO4pCjBsGHDwoFjBe/t2sbIzkb2RmR/XrAH69BElUV54Afjvf4FSk1CIiIiIXIJKUSrqgrKu2NhYtG3bFkuWiA4+nU6HqKgojBo1ChMnTnygfb9+/aDRaLB9+3b9fU888QRatmyJzz77rMLz5eTkIDAwEEA21G4+8NRlQgXg2e5iWp+Htye8AoPg7g6o1YA2J93oNC83d3d4BBg+QRbdtqBtVgZUMD6N2TMotExbKOa1Lc4R05jLi0OlArxCwirVtignC8rdooduCwBewYYpz8W5OdAVFUClAtzyNWj7l6YoKMxHHU9vfPOvswiqZZjyfFdjaFv63CU8A0Og9nCHSgVo83OhLciDWi2Ko7u5Qb+vVgOBESHw9XeHlxdwN8/09OjS05grmkrNKc8PtnWEKc+aggKER0cjOzsbAfeeYw9snR8BQ46shotQwR9PdwM+/RQIDBSPyygLodOJiwR374oLFjpF5D2tVtzOy8iATquDTmcYdlpSJULl5gavYEOOLMwyXerBu3qp8g23M6FoRdvS75Q6nTivZ3CYPob821m4W1ikv10Sq1YrnusRFKaP7W6uyJHGuAcYcqQ2T+Q9ACjOuYVebzcDAPzf4tPwCKhhtG151H6GshB383KhK3wwlx09Ciz7DChUheC7He545hnL8h5zJHOkrcjMj0A2gAAMGQJ8/jnLQNgDTXo6EB4ubqSllSnFQ2RNJXnBnvIjEZHNyBweWVhYqKjVamXLli1l7h84cKDSq1evcp8TFRWlLCw1rVRRFGXKlClK8+bNy21fUFCgZGdn67fr168rABQgWwEUpWVLRfnhh6p4NUTkqOxxuoot8qOiGM+R2Uamh//Xu4YydKgoAfDqq6bLQhxwD1SeekpRnnhC5FpTZSGOw0cJDVWU4GBFCQgwXRbiNLzK3HUapstClL7rGEyXhSh91x4EGm2bC5S5aztMl4UoffMbmC4L4YM0/c0ElF8WomQLxVn9zSUwXRYiGgf0Nz+C6bIQi4dv1f+O7OnSxWTbM4mJhrbPsSyEorAshLXJzo9AtjJpkqLodA/7SojI0dnjZ0giIluRek01IyMDWq0W4SVXFO8JDw/H+fPny31Oampque1TU1PLbT9nzhxMnz79gft9fYElS8TKlWp1JV8AEZGV2CI/AsZzpDEFBcDKlYbbi020vXsX2LPHvOMqAEwMkDLKzQ0wMthbz99f/KtSATA+GA2AmOZbQlVB2/Bw8f6hVgPqm6bjiI01xOB5EkCx8bZtWgPFYhAfvJNgWPWpHC1bABoxgA4+yQDyjbdt2gSoFSj2/X4BoDHetkcP448RySY7P374ITB+fCUCJyIiInIiTj9JY9KkSYiPj9ffzsnJQVRUFM6eFSuWEhG5MmM5cv7Yi/D28n+gveLmidk+htuLNWlQqfDA5u0NVPN1x7pQoFo1cTtJk6ovQXB/GQJ3T3ecrm7ooMvLTsWvah08PO497m54LNLDDXfDxXNVKiAv4wY0uvJ788Lc3JBjmPGM/Mzr0BiZ8uwDIDusdNvLRtsCQGqptgVZv0JjYnX7I2XanjbZdm9oKFT3KhwX5iRBY2IV8v+Uafujybb/DgmB2713/aLcPdCYmJr8SKnp5+23bzfZNqbUFPj2GzaYbPtY6baJidDcm65ansdL9e7GLl4MzezZRtu2LdW27ezZ0JQz5VV/rHvThwHg8YkToXnrLaNt25dq+9hbb0Hzt78Zb+tj+MOI6d8fGhO9sqXbPvL889CkpZnVttYTT5hs+8S9ad8AUD0mxmTbtqXa+oSGmmzbvKAAiI42+rizMpYf33hDYlBEREREdkJqh2JoaCjUajXS7vsQm5aWhggjq19ERERY1N7LywteXl4P3F/qOw3Zi4ICaPv0QdLJk5jVqhXWb90K71JfeIhciS3yI2A8R46bUsPMWkCW1KmypG1oxU3u8Qk1v201C1ZOsKSttwVvKpa09QoI0Ne5K8jKwqlGjQAAj/366wPHKd22Ip5+fvq6fLLaevj46OsTVmVbd29vfT3Fqmyr9vQ0uy6bJW3d3N2t0lbl5lZlbbUm6kzKIDs/kv2pKD8SERFR1ZO6yrOnpydat26N3bt36+/T6XTYvXs34uLiyn1OXFxcmfYAsGvXLqPtyYFotVDv2IG2t25h186d0Fa0XDSRE2N+pPtpi4rQ9tYttL11C1oTIxyJnB3zI92P+ZGIiMj2pE95jo+Px6BBg9CmTRu0a9cOixYtgkajwZAhQwAAAwcORGRkJObMmQMAGD16NLp06YIFCxbg+eefx/r163HixAl8/vnnMl8GEVGVY34kIiof8yMRERGRXNI7FPv164dbt25hypQpSE1NRcuWLbFjxw594exr167Bzc0wkLJ9+/b4+uuv8f777+Pdd99Fw4YNsXXrVjRr1kzWSyAisgrmRyKi8jE/EhEREcmlUhRFkR2ELeXk5CAwMBDZ2dlm1gcjm9FogHu1t3wBpOfmwtfXV25M5BKYFwz4s7BfmvR0+N7rLNGkpZldH4/oYTEvCPw52C/mR5KFeYGIXJnUGopERERERERERETkWNihSERERERERERERGaTXkPR1kpmeOfk5EiOhB6g0eh3FYj/I670TLZQkg9crAJEuZgj7Zfmzh1oS+97e0uNh1wHc6TA/Gi/mB9JFuZHInJlLteheOfOHQBAVFSU5EioIrVq1ZIdArmYO3fuIDAwUHYYUjFHOogGDWRHQC7I1XMk86ODYH4kCVw9PxKRa3K5RVl0Oh1+//13+Pv7Q6VSyQ6nQjk5OYiKisL169cdotCvo8ULOF7MjLfqKYqCO3fuoFatWmVWBXVFzJHWxXiti/FaB3OkwPxoXYzXuhivdTA/EpErc7kRim5ubqhdu7bsMCwWEBBg12+m93O0eAHHi5nxVi1eVRaYI22D8VoX4616zJHMj7bCeK2L8VY95kciclW8jEJERERERERERERmY4ciERERERERERERmY0dinbOy8sLU6dOhZeXl+xQzOJo8QKOFzPjJTJwtN8vxmtdjJfIwNF+vxivdTFeIiKqai63KAsRERERERERERFVHkcoEhERERERERERkdnYoUhERERERERERERmY4ciERERERERERERmY0dinZq//796NmzJ2rVqgWVSoWtW7fKDsmkOXPmoG3btvD390dYWBh69+6N//3vf7LDMmrZsmVo3rw5AgICEBAQgLi4OHz//feywzLb3LlzoVKpMGbMGNmhlGvatGlQqVRltsaNG8sOi5yII+VIR8uPgGPnSHvPjwBzJFmXI+VHwPFypCPnR8D+cyTzIxGR42CHop3SaDRo0aIFli5dKjsUs+zbtw8jRozAkSNHsGvXLhQXF+OZZ56BRqORHVq5ateujblz5+LkyZM4ceIE/vSnP+HPf/4zzpw5Izu0Ch0/fhzLly9H8+bNZYdiUtOmTZGSkqLfDh48KDskciKOlCMdLT8CjpsjHSU/AsyRZD2OlB8Bx8uRjpofAcfJkcyPRESOwV12AFS+Hj16oEePHrLDMNuOHTvK3E5MTERYWBhOnjyJzp07S4rKuJ49e5a5PWvWLCxbtgxHjhxB06ZNJUVVsdzcXAwYMAArVqzAzJkzZYdjkru7OyIiImSHQU7KkXKko+VHwDFzpCPlR4A5kqzHkfIj4Hg50hHzI+BYOZL5kYjIMXCEIllFdnY2ACAkJERyJBXTarVYv349NBoN4uLiZIdj0ogRI/D888+jW7duskOp0IULF1CrVi088sgjGDBgAK5duyY7JCK74Ej5EXCcHOlI+RFgjiQyxpFypKPkR8CxciTzIxGRY+AIRapyOp0OY8aMQYcOHdCsWTPZ4Rh16tQpxMXFoaCgAH5+ftiyZQuaNGkiOyyj1q9fj6SkJBw/flx2KBWKjY1FYmIiYmJikJKSgunTp6NTp044ffo0/P39ZYdHJI2j5EfAsXKkI+VHgDmSyBhHyZGOlB8Bx8qRzI9ERI6DHYpU5UaMGIHTp0/bfb2TmJgYJCcnIzs7Gxs3bsSgQYOwb98+u/xAeP36dYwePRq7du2Ct7e37HAqVHqqVfPmzREbG4vo6Gh88803GDp0qMTIiORylPwIOE6OdLT8CDBHEhnjKDnSUfIj4Hg5kvmRiMhxsEORqtTIkSOxfft27N+/H7Vr15Ydjkmenp5o0KABAKB169Y4fvw4PvnkEyxfvlxyZA86efIk0tPT0apVK/19Wq0W+/fvx5IlS1BYWAi1Wi0xQtOCgoLQqFEjXLx4UXYoRNI4Un4EHCdHOnp+BJgjiQDHypGOkh8Bx8+RzI9ERPaLHYpUJRRFwahRo7Blyxbs3bsX9erVkx2SxXQ6HQoLC2WHUa6uXbvi1KlTZe4bMmQIGjdujAkTJtj1B0FAFAK/dOkSXnnlFdmhENmcM+RHwH5zpKPnR4A5klybM+RIe82PgOPnSOZHIiL7xQ5FO5Wbm1vmStyVK1eQnJyMkJAQ1KlTR2Jk5RsxYgS+/vprbNu2Df7+/khNTQUABAYGolq1apKje9CkSZPQo0cP1KlTB3fu3MHXX3+NvXv3YufOnbJDK5e/v/8DtYR8fX1RvXp1u6wxNG7cOPTs2RPR0dH4/fffMXXqVKjVavTv3192aOQkHClHOlp+BBwrRzpafgSYI8m6HCk/Ao6XIx0pPwKOlyOZH4mIHAc7FO3UiRMn8NRTT+lvx8fHAwAGDRqExMRESVEZt2zZMgDAk08+Web+hIQEDB482PYBVSA9PR0DBw5ESkoKAgMD0bx5c+zcuRNPP/207NCcwo0bN9C/f3/88ccfqFGjBjp27IgjR46gRo0askMjJ+FIOdLR8iPAHGltzJFkTY6UHwHHy5HMj9bF/EhE5DhUiqIosoMgIiIiIiIiIiIix+AmOwAiIiIiIiIiIiJyHOxQJCIiIiIiIiIiIrOxQ5GIiIiIiIiIiIjMxg5FIiIiIiIiIiIiMhs7FImIiIiIiIiIiMhs7FAkIiIiIiIiIiIis7FDkYiIiIiIiIiIiMzGDkUiIiIiIiIiIiIyGzsUqdKuXr0KlUqF5ORks58zePBg9O7d22SbJ598EmPGjHmo2FQqFbZu3QrA/DjNOW/p49rStGnToFKpoFKpsGjRooc6VmJiIoKCgmx2PiJXxRxpO8yRRI6F+dF2mB+JiMha2KHoxFJTUzFq1Cg88sgj8PLyQlRUFHr27Indu3fLDs2moqKikJKSgmbNmgEA9u7dC5VKhaysLIuPlZKSgh49elRxhOZp2rQpUlJS8Prrrz/w2Jw5c6BWqzFv3rwqOde4ceOQkpKC2rVrV8nxiOwRc6TAHGk55khydsyPAvOj5ZgfiYhcBzsUndTVq1fRunVr/PDDD5g3bx5OnTqFHTt24KmnnsKIESNkh2dTarUaERERcHd3f+hjRUREwMvLqwqispy7uzsiIiLg4+PzwGOrVq3C+PHjsWrVqio5l5+fHyIiIqBWq6vkeET2hjnSgDnScsyR5MyYHw2YHy3H/EhE5DrYoeik3nrrLahUKhw7dgx9+/ZFo0aN0LRpU8THx+PIkSMAgFdffRUvvPBCmecVFxcjLCwMK1euBADodDp89NFHaNCgAby8vFCnTh3MmjWr3HNqtVoMHToU9erVQ7Vq1RATE4NPPvmk3LbTp09HjRo1EBAQgDfeeANFRUVGX0thYSHGjRuHyMhI+Pr6IjY2Fnv37jX7Z1F6usrVq1fx1FNPAQCCg4OhUqkwePBgfVudTofx48cjJCQEERERmDZtWpljlZ6uUt5V6uTkZKhUKly9ehWAYWrI9u3bERMTAx8fH7z00kvIy8vD6tWrUbduXQQHB+Ptt9+GVqs1+zWVtm/fPuTn52PGjBnIycnBoUOHzHrezp078eijj8LPzw/PPvssUlJSKnV+IkfEHGnAHFk+5khyVcyPBsyP5WN+JCIiAHj4y21kdzIzM7Fjxw7MmjULvr6+DzxeUvvktddeQ+fOnZGSkoKaNWsCALZv3468vDz069cPADBp0iSsWLECCxcuRMeOHZGSkoLz58+Xe16dTofatWvjX//6F6pXr45Dhw7h9ddfR82aNfGXv/xF32737t3w9vbG3r17cfXqVQwZMgTVq1c3+iFz5MiROHv2LNavX49atWphy5YtePbZZ3Hq1Ck0bNjQop9NVFQUNm3ahL59++J///sfAgICUK1aNf3jq1evRnx8PI4ePYrDhw9j8ODB6NChA55++mmLzlNaXl4eFi9ejPXr1+POnTt48cUX0adPHwQFBeG7777D5cuX0bdvX3To0EH/c7fEypUr0b9/f3h4eKB///5YuXIl2rdvX2FM8+fPx9q1a+Hm5oa///3vGDduHL766qvKvkwih8EcaRxzpCEm5khyRcyPxjE/GmJifiQiIgCAQk7n6NGjCgBl8+bNFbZt0qSJ8uGHH+pv9+zZUxk8eLCiKIqSk5OjeHl5KStWrCj3uVeuXFEAKD/99JPR448YMULp27ev/vagQYOUkJAQRaPR6O9btmyZ4ufnp2i1WkVRFKVLly7K6NGjFUVRlN9++01Rq9XKzZs3yxy3a9euyqRJk4yeF4CyZcuWcuPcs2ePAkC5fft2med06dJF6dixY5n72rZtq0yYMKHc45Z3nJ9++kkBoFy5ckVRFEVJSEhQACgXL17Utxk+fLji4+Oj3LlzR39f9+7dleHDhxt9PVOnTlVatGjxwP3Z2dlKtWrVlOTkZP35/fz8yhz7fuXFtHTpUiU8PPyBttHR0crChQuNHovIETFHMkcyRxKVj/mR+ZH5kYiIzMUpz05IURSz27722mtISEgAAKSlpeH777/Hq6++CgA4d+4cCgsL0bVrV7OPt3TpUrRu3Ro1atSAn58fPv/8c1y7dq1MmxYtWpSp4RIXF4fc3Fxcv379geOdOnUKWq0WjRo1gp+fn37bt28fLl26ZHZc5mrevHmZ2zVr1kR6evpDHdPHxwf169fX3w4PD0fdunXh5+dX5r7KnGfdunWoX78+WrRoAQBo2bIloqOjsWHDBotiqorXSeQomCMrjzmSyLkxP1Ye8yMREbkaTnl2Qg0bNoRKpTI6raS0gQMHYuLEiTh8+DAOHTqEevXqoVOnTgBQZhqHOdavX49x48ZhwYIFiIuLg7+/P+bNm4ejR49W6nUAQG5uLtRqNU6ePPlAcefSH6aqioeHR5nbKpUKOp2u3LZubqI/vvSH7+LiYrOOacl5TFm5ciXOnDlTpli4TqfDqlWrMHToUKPPK+/8lnyJIHJkzJGVxxxJ5NyYHyuP+ZGIiFwNOxSdUEhICLp3746lS5fi7bfffqAGTlZWlr4GTvXq1dG7d28kJCTg8OHDGDJkiL5dw4YNUa1aNezevRuvvfZahef98ccf0b59e7z11lv6+8q7Avzzzz8jPz9f/2HzyJEj8PPzQ1RU1ANtH3/8cWi1WqSnp+s/pD4sT09PAKh0AesSNWrUAACkpKQgODgYgCiobSunTp3CiRMnsHfvXoSEhOjvz8zMxJNPPonz58+jcePGNouHyFEwR5rGHEnkupgfTWN+JCIiMuCUZye1dOlSaLVatGvXDps2bcKFCxdw7tw5LF68GHFxcWXavvbaa1i9ejXOnTuHQYMG6e/39vbGhAkTMH78eKxZswaXLl3CkSNH9Kv33a9hw4Y4ceIEdu7ciV9//RWTJ0/G8ePHH2hXVFSEoUOH4uzZs/juu+8wdepUjBw5Un+1trRGjRphwIABGDhwIDZv3owrV67g2LFjmDNnDr799ttK/Wyio6OhUqmwfft23Lp1C7m5uZU6ToMGDRAVFYVp06bhwoUL+Pbbb7FgwYJKHasyVq5ciXbt2qFz585o1qyZfuvcuTPatm2r/39asmSJRVOOiFwBc6RxzJFEro350TjmRyIiIgN2KDqpRx55BElJSXjqqacwduxYNGvWDE8//TR2796NZcuWlWnbrVs31KxZE927d0etWrXKPDZ58mSMHTsWU6ZMwaOPPop+/foZrZMyfPhwvPjii+jXrx9iY2Pxxx9/lLnSXKJr165o2LAhOnfujH79+qFXr16YNm2a0deSkJCAgQMHYuzYsYiJiUHv3r1x/Phx1KlTx/IfDIDIyEhMnz4dEydORHh4OEaOHFmp43h4eGDdunU4f/48mjdvjg8//BAzZ86s1LEsVVRUhC+//BJ9+/Yt9/G+fftizZo1KC4uRkZGhlVqBRE5MuZI45gjiVwb86NxzI9EREQGKoVFL1xebm4uIiMjkZCQgBdffFF2OFSOadOmYevWrTadDgMAdevWxZgxYzBmzBibnpfInjBH2j/mSCI5mB/tH/MjERFZC0coujCdTof09HR88MEHCAoKQq9evWSHRCacOnUKfn5++PTTT61+rtmzZ8PPz++B1RWJXAlzpGNhjiSyHeZHx8L8SERE1sARii7s6tWrqFevHmrXro3ExETWSLFjmZmZyMzMBCAKeQcGBjrV+YjsEXOk42COJLIt5kfHwfxIRETWwg5FIiIiIiIiIiIiMhunPBMREREREREREZHZ2KFIREREREREREREZmOHIhEREREREREREZmNHYpERERERERERERkNnYoEhERERERERERkdnYoUhERERERERERERmY4ciERERERERERERmY0dikRERERERERERGQ2digSERERERERERGR2f4fKMuvQ5sJ/LYAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -557,14 +561,16 @@ } ], "source": [ + "# Skip the MSMR example parameter set since we need to set up the ESOH solver differently\n", + "all_parameter_sets.remove(\"MSMR_Example\")\n", + "# Loop over all parameter sets and solve the ESOH problem\n", "for parameter_set in all_parameter_sets:\n", " print(parameter_set)\n", " try:\n", " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n", " fig, axes = plot_sweep(sweep, sol_init_QLi, sol_init_Q, parameter_set)\n", " except ValueError:\n", - " pass\n", - " # print(\"success\")" + " pass" ] }, { @@ -579,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -588,26 +594,26 @@ "text": [ "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[3] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", "[5] Madeleine Ecker, Stefan KΓ€bitz, Izaro Laresgoiti, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: II. Model Validation. Journal of The Electrochemical Society, 162(9):A1849–A1857, 2015. doi:10.1149/2.0541509jes.\n", "[6] Madeleine Ecker, Thi Kim Dung Tran, Philipp Dechent, Stefan KΓ€bitz, Alexander Warnecke, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters. Journal of the Electrochemical Society, 162(9):A1836–A1848, 2015. doi:10.1149/2.0551509jes.\n", "[7] Alastair Hales, Laura Bravo Diaz, Mohamed Waseem Marzook, Yan Zhao, Yatish Patel, and Gregory Offer. The cell cooling coefficient: a standard to define heat rejection from lithium-ion batteries. Journal of The Electrochemical Society, 166(12):A2383, 2019.\n", "[8] Charles R. Harris, K. Jarrod Millman, StΓ©fan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[9] Gi-Heon Kim, Kandler Smith, Kyu-Jin Lee, Shriram Santhanagopalan, and Ahmad Pesaran. Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of the Electrochemical Society, 158(8):A955–A969, 2011. doi:10.1149/1.3597614.\n", - "[10] Michael J. Lain, James Brandon, and Emma Kendrick. Design strategies for high power vs. high energy lithium ion cells. Batteries, 5(4):64, 2019. doi:10.3390/batteries5040064.\n", - "[11] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[12] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[13] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", - "[14] Andreas Nyman, MΓ₯rten Behm, and GΓΆran Lindbergh. Electrochemical characterisation and modelling of the mass transport phenomena in lipf6–ec–emc electrolyte. Electrochimica Acta, 53(22):6356–6365, 2008.\n", - "[15] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[16] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", - "[17] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[18] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, ValΓ©rie Sauvant-Moynot, and FranΓ§ois Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", - "[19] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", - "[20] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", - "[21] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[22] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[10] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[11] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[12] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", + "[13] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[14] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", + "[15] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[16] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, ValΓ©rie Sauvant-Moynot, and FranΓ§ois Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", + "[17] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", + "[18] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", + "[19] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[20] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", + "[21] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[22] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "[23] Yan Zhao, Yatish Patel, Teng Zhang, and Gregory J Offer. Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance. Journal of The Electrochemical Society, 165(13):A3169, 2018.\n", "\n" ] @@ -620,7 +626,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -651,7 +657,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb index f03c328abf..933d27aa78 100644 --- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb +++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb @@ -280,23 +280,36 @@ "outputs": [], "source": [ "# define spiral \n", + "\n", + "\n", "def spiral_pos_inner(t):\n", " return r0 - eps * delta + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_pos_outer(t):\n", " return r0 + eps * delta + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_neg_inner(t):\n", " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_neg_outer(t):\n", " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_am1_inner(t):\n", " return r0 + eps * delta + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_am1_outer(t):\n", " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_am2_inner(t):\n", " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_am2_outer(t):\n", " return r0 - eps * delta + eps + eps * t / (2 * pi)" ] diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb index f1e81796a1..7eae36e725 100644 --- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb @@ -312,6 +312,7 @@ "def current_LAM(i, T):\n", " return -1e-10 * (abs(i) + 1e3 * abs(i) ** 0.5)\n", "\n", + "\n", "model = pybamm.lithium_ion.DFN(\n", " options=\n", " {\n", diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index b7315a62e4..35226ed89f 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -1,1817 +1,1770 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterisation\n", - "\n", - "In this notebook, we show how to find which parameters are needed in a model and define them.\n", - "\n", - "For other notebooks about parameterization, see:\n", - "\n", - "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n", - "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", - "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding your own parameter sets (using a dictionary)\n", - "\n", - "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the model\n", - "\n", - "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", - "\n", - "R = pybamm.Parameter(\"Particle radius [m]\")\n", - "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", - "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", - "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", - "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.BaseModel()\n", - "\n", - "# governing equations\n", - "N = -D * pybamm.grad(c) # flux\n", - "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", - "\n", - "# boundary conditions \n", - "lbc = pybamm.Scalar(0)\n", - "rbc = -j\n", - "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", - "\n", - "# initial conditions \n", - "model.initial_conditions = {c: c0}\n", - "\n", - "model.variables = {\n", - " \"Concentration [mol.m-3]\": c,\n", - " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", - " \"Flux [mol.m-2.s-1]\": N,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also define the geometry, since there are parameters in the geometry too" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", - "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters required" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To know what parameters are required by the model and geometry, we can do" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial concentration [mol.m-3] (Parameter)\n", - "Interfacial current density [A.m-2] (InputParameter)\n", - "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n", - "\n", - "Particle radius [m] (Parameter)\n" - ] - } - ], - "source": [ - "model.print_parameter_info()\n", - "geometry.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding the parameters\n", - "\n", - "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def D_fun(c):\n", - " return 3.9 #* pybamm.exp(-c)\n", - "\n", - "values = {\n", - " \"Particle radius [m]\": 2,\n", - " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", - " \"Initial concentration [mol.m-3]\": 2.5,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 2.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param = pybamm.ParameterValues(values)\n", - "\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating the parameter values\n", - "\n", - "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 1.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", - " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", - " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parameters = model.parameters\n", - "parameters" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "param.process_model(model)\n", - "param.process_geometry(geometry)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now set up our mesh, choose a spatial method, and discretise our model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", - "var_pts = {r: 20}\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", - "\n", - "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", - "disc = pybamm.Discretisation(mesh, spatial_methods)\n", - "disc.process_model(model);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# solve\n", - "solver = pybamm.ScipySolver()\n", - "t = np.linspace(0, 3600, 600)\n", - "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", - "\n", - "# post-process, so that the solution can be called at any time t or space r\n", - "# (using interpolation)\n", - "c = solution[\"Concentration [mol.m-3]\"]\n", - "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", - "\n", - "# plot\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", - "\n", - "ax1.plot(solution.t, c_surf(solution.t))\n", - "ax1.set_xlabel(\"Time [s]\")\n", - "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", - "\n", - "rsol = mesh[\"negative particle\"].nodes # radial position\n", - "time = 1000 # time in seconds\n", - "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", - "ax2.set_xlabel(\"Particle radius [microns]\")\n", - "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", - "ax2.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using pre-defined models in `PyBaMM`\n", - "\n", - "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", - "\n", - "We start by initializing our model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "spm = pybamm.lithium_ion.SPM()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "We can print the `parameters` of a model by using the `get_parameters_info` function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", - "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", - "Separator thickness [m] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Negative electrode thickness [m] (Parameter)\n", - "Electrode height [m] (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Number of cells connected in series to make a battery (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Lower voltage cut-off [V] (Parameter)\n", - "Nominal cell capacity [A.h] (Parameter)\n", - "Typical electrolyte concentration [mol.m-3] (Parameter)\n", - "Upper voltage cut-off [V] (Parameter)\n", - "Positive electrode electrons in reaction (Parameter)\n", - "Negative electrode electrons in reaction (Parameter)\n", - "Initial temperature [K] (Parameter)\n", - "Reference temperature [K] (Parameter)\n", - "Positive electrode thickness [m] (Parameter)\n", - "Number of electrodes connected in parallel to make a cell (Parameter)\n", - "Electrode width [m] (Parameter)\n", - "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", - "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", - "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", - "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n", - "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n", - "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", - "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", - "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", - "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", - "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", - "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", - "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "\n" - ] - } - ], - "source": [ - "spm.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Negative electrode thickness [m]': 0.0001,\n", - " 'Separator thickness [m]': 2.5e-05,\n", - " 'Positive electrode thickness [m]': 0.0001,\n", - " 'Electrode height [m]': 0.137,\n", - " 'Electrode width [m]': 0.207,\n", - " 'Nominal cell capacity [A.h]': 0.680616,\n", - " 'Current function [A]': 0.680616,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", - " 'Negative electrode OCP [V]': ,\n", - " 'Negative electrode porosity': 0.3,\n", - " 'Negative electrode active material volume fraction': 0.6,\n", - " 'Negative particle radius [m]': 1e-05,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': ,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", - " 'Positive electrode OCP [V]': ,\n", - " 'Positive electrode porosity': 0.3,\n", - " 'Positive electrode active material volume fraction': 0.5,\n", - " 'Positive particle radius [m]': 1e-05,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': ,\n", - " 'Separator porosity': 1.0,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 3.105,\n", - " 'Upper voltage cut-off [V]': 4.1,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Ambient temperature [K]': 298.15,\n", - " 'Current function [A]': 5.0,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n", - " ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n", - " 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n", - " 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n", - " 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n", - " 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n", - " 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n", - " 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n", - " 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n", - " 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n", - " 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n", - " 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n", - " 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n", - " 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n", - " 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n", - " 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n", - " 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n", - " 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n", - " 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n", - " 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n", - " 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n", - " 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n", - " 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n", - " 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n", - " 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n", - " 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n", - " 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n", - " 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n", - " 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n", - " 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n", - " 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n", - " 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n", - " 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n", - " 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n", - " 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n", - " 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n", - " 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n", - " 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n", - " 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n", - " 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n", - " 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n", - " 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n", - " 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n", - " 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n", - " 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n", - " 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n", - " 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n", - " 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n", - " 0.89774404, 0.9014468 , 1. ])],\n", - " array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n", - " 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n", - " 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n", - " 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n", - " 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n", - " 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n", - " 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n", - " 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n", - " 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n", - " 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n", - " 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n", - " 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n", - " 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n", - " 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n", - " 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n", - " 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n", - " 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n", - " 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n", - " 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n", - " 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n", - " 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n", - " 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n", - " 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n", - " 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n", - " 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n", - " 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n", - " 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n", - " 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n", - " 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n", - " 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n", - " 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n", - " 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n", - " 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n", - " 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n", - " 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n", - " 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n", - " 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n", - " 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n", - " 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n", - " 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n", - " 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n", - " 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n", - " 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n", - " 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n", - " 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n", - " 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n", - " 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n", - " 0.08709427, 0.08503284, 0.07601531]))),\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n", - " ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n", - " 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n", - " 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n", - " 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n", - " 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n", - " 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n", - " 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n", - " 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n", - " 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n", - " 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n", - " 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n", - " 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n", - " 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n", - " 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n", - " 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n", - " 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n", - " 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n", - " 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n", - " 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n", - " 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n", - " 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n", - " 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n", - " 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n", - " 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n", - " 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n", - " 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n", - " 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n", - " 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n", - " 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n", - " 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n", - " 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n", - " 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n", - " 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n", - " 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n", - " 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n", - " 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n", - " 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n", - " 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n", - " 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n", - " 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n", - " 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n", - " 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n", - " 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n", - " 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n", - " 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n", - " 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n", - " 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n", - " 0.90320364, 0.90592613, 1. ])],\n", - " array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n", - " 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n", - " 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n", - " 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n", - " 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n", - " 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n", - " 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n", - " 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n", - " 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n", - " 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n", - " 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n", - " 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n", - " 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n", - " 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n", - " 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n", - " 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n", - " 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n", - " 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n", - " 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n", - " 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n", - " 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n", - " 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n", - " 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n", - " 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n", - " 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n", - " 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n", - " 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n", - " 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n", - " 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n", - " 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n", - " 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n", - " 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n", - " 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n", - " 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n", - " 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n", - " 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n", - " 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n", - " 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n", - " 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n", - " 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n", - " 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n", - " 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n", - " 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n", - " 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n", - " 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n", - " 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n", - " 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n", - " 3.5684922 , 3.5672133 , 3.52302167]))),\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Typical current [A]': 5.0,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Upper voltage cut-off [V]': 4.4}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", - " D_ref = 3.9 * 10 ** (-14)\n", - " E_D_s = 42770\n", - " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", - " return D_ref * arrhenius\n", - "\n", - "neg_ocp = np.array([[0. , 1.81772748],\n", - " [0.03129623, 1.0828807 ],\n", - " [0.03499902, 0.99593794],\n", - " [0.0387018 , 0.90023398],\n", - " [0.04240458, 0.79649431],\n", - " [0.04610736, 0.73354429],\n", - " [0.04981015, 0.66664314],\n", - " [0.05351292, 0.64137149],\n", - " [0.05721568, 0.59813869],\n", - " [0.06091845, 0.5670836 ],\n", - " [0.06462122, 0.54746181],\n", - " [0.06832399, 0.53068399],\n", - " [0.07202675, 0.51304734],\n", - " [0.07572951, 0.49394092],\n", - " [0.07943227, 0.47926274],\n", - " [0.08313503, 0.46065259],\n", - " [0.08683779, 0.45992726],\n", - " [0.09054054, 0.43801501],\n", - " [0.09424331, 0.42438665],\n", - " [0.09794607, 0.41150269],\n", - " [0.10164883, 0.40033659],\n", - " [0.10535158, 0.38957134],\n", - " [0.10905434, 0.37756538],\n", - " [0.1127571 , 0.36292541],\n", - " [0.11645985, 0.34357086],\n", - " [0.12016261, 0.3406314 ],\n", - " [0.12386536, 0.32299468],\n", - " [0.12756811, 0.31379458],\n", - " [0.13127086, 0.30795386],\n", - " [0.13497362, 0.29207319],\n", - " [0.13867638, 0.28697687],\n", - " [0.14237913, 0.27405477],\n", - " [0.14608189, 0.2670497 ],\n", - " [0.14978465, 0.25857493],\n", - " [0.15348741, 0.25265783],\n", - " [0.15719018, 0.24826777],\n", - " [0.16089294, 0.2414345 ],\n", - " [0.1645957 , 0.23362778],\n", - " [0.16829847, 0.22956218],\n", - " [0.17200122, 0.22370236],\n", - " [0.17570399, 0.22181271],\n", - " [0.17940674, 0.22089651],\n", - " [0.1831095 , 0.2194268 ],\n", - " [0.18681229, 0.21830064],\n", - " [0.19051504, 0.21845333],\n", - " [0.1942178 , 0.21753715],\n", - " [0.19792056, 0.21719357],\n", - " [0.20162334, 0.21635373],\n", - " [0.2053261 , 0.21667822],\n", - " [0.20902886, 0.21738444],\n", - " [0.21273164, 0.21469313],\n", - " [0.2164344 , 0.21541846],\n", - " [0.22013716, 0.21465495],\n", - " [0.22383993, 0.2135479 ],\n", - " [0.2275427 , 0.21392964],\n", - " [0.23124547, 0.21074206],\n", - " [0.23494825, 0.20873788],\n", - " [0.23865101, 0.20465319],\n", - " [0.24235377, 0.20205732],\n", - " [0.24605653, 0.19774358],\n", - " [0.2497593 , 0.19444147],\n", - " [0.25346208, 0.19190285],\n", - " [0.25716486, 0.18850531],\n", - " [0.26086762, 0.18581399],\n", - " [0.26457039, 0.18327537],\n", - " [0.26827314, 0.18157659],\n", - " [0.2719759 , 0.17814088],\n", - " [0.27567867, 0.17529686],\n", - " [0.27938144, 0.1719375 ],\n", - " [0.28308421, 0.16934161],\n", - " [0.28678698, 0.16756649],\n", - " [0.29048974, 0.16609676],\n", - " [0.29419251, 0.16414985],\n", - " [0.29789529, 0.16260378],\n", - " [0.30159806, 0.16224113],\n", - " [0.30530083, 0.160027 ],\n", - " [0.30900361, 0.15827096],\n", - " [0.31270637, 0.1588054 ],\n", - " [0.31640913, 0.15552238],\n", - " [0.32011189, 0.15580869],\n", - " [0.32381466, 0.15220118],\n", - " [0.32751744, 0.1511132 ],\n", - " [0.33122021, 0.14987253],\n", - " [0.33492297, 0.14874637],\n", - " [0.33862575, 0.14678037],\n", - " [0.34232853, 0.14620776],\n", - " [0.34603131, 0.14555879],\n", - " [0.34973408, 0.14389819],\n", - " [0.35343685, 0.14359279],\n", - " [0.35713963, 0.14242846],\n", - " [0.36084241, 0.14038612],\n", - " [0.36454517, 0.13882096],\n", - " [0.36824795, 0.13954628],\n", - " [0.37195071, 0.13946992],\n", - " [0.37565348, 0.13780934],\n", - " [0.37935626, 0.13973714],\n", - " [0.38305904, 0.13698858],\n", - " [0.38676182, 0.13523254],\n", - " [0.3904646 , 0.13441178],\n", - " [0.39416737, 0.1352898 ],\n", - " [0.39787015, 0.13507985],\n", - " [0.40157291, 0.13647321],\n", - " [0.40527567, 0.13601512],\n", - " [0.40897844, 0.13435452],\n", - " [0.41268121, 0.1334765 ],\n", - " [0.41638398, 0.1348317 ],\n", - " [0.42008676, 0.13275118],\n", - " [0.42378953, 0.13286571],\n", - " [0.4274923 , 0.13263667],\n", - " [0.43119506, 0.13456447],\n", - " [0.43489784, 0.13471718],\n", - " [0.43860061, 0.13395369],\n", - " [0.44230338, 0.13448814],\n", - " [0.44600615, 0.1334765 ],\n", - " [0.44970893, 0.13298023],\n", - " [0.45341168, 0.13259849],\n", - " [0.45711444, 0.13338107],\n", - " [0.46081719, 0.13309476],\n", - " [0.46451994, 0.13275118],\n", - " [0.46822269, 0.13443087],\n", - " [0.47192545, 0.13315202],\n", - " [0.47562821, 0.132713 ],\n", - " [0.47933098, 0.1330184 ],\n", - " [0.48303375, 0.13278936],\n", - " [0.48673651, 0.13225491],\n", - " [0.49043926, 0.13317111],\n", - " [0.49414203, 0.13263667],\n", - " [0.49784482, 0.13187316],\n", - " [0.50154759, 0.13265574],\n", - " [0.50525036, 0.13250305],\n", - " [0.50895311, 0.13324745],\n", - " [0.51265586, 0.13204496],\n", - " [0.51635861, 0.13242669],\n", - " [0.52006139, 0.13233127],\n", - " [0.52376415, 0.13198769],\n", - " [0.52746692, 0.13254122],\n", - " [0.53116969, 0.13145325],\n", - " [0.53487245, 0.13298023],\n", - " [0.53857521, 0.13168229],\n", - " [0.54227797, 0.1313578 ],\n", - " [0.54598074, 0.13235036],\n", - " [0.5496835 , 0.13120511],\n", - " [0.55338627, 0.13089971],\n", - " [0.55708902, 0.13109058],\n", - " [0.56079178, 0.13082336],\n", - " [0.56449454, 0.13011713],\n", - " [0.5681973 , 0.129869 ],\n", - " [0.57190006, 0.12992626],\n", - " [0.57560282, 0.12942998],\n", - " [0.57930558, 0.12796026],\n", - " [0.58300835, 0.12862831],\n", - " [0.58671112, 0.12656689],\n", - " [0.59041389, 0.12734947],\n", - " [0.59411664, 0.12509716],\n", - " [0.59781941, 0.12110791],\n", - " [0.60152218, 0.11839751],\n", - " [0.60522496, 0.11244226],\n", - " [0.60892772, 0.11307214],\n", - " [0.61263048, 0.1092165 ],\n", - " [0.61633325, 0.10683058],\n", - " [0.62003603, 0.10433014],\n", - " [0.6237388 , 0.10530359],\n", - " [0.62744156, 0.10056993],\n", - " [0.63114433, 0.09950104],\n", - " [0.63484711, 0.09854668],\n", - " [0.63854988, 0.09921473],\n", - " [0.64225265, 0.09541635],\n", - " [0.64595543, 0.09980643],\n", - " [0.64965823, 0.0986612 ],\n", - " [0.653361 , 0.09560722],\n", - " [0.65706377, 0.09755413],\n", - " [0.66076656, 0.09612258],\n", - " [0.66446934, 0.09430929],\n", - " [0.66817212, 0.09661885],\n", - " [0.67187489, 0.09366032],\n", - " [0.67557767, 0.09522548],\n", - " [0.67928044, 0.09535909],\n", - " [0.68298322, 0.09316404],\n", - " [0.686686 , 0.09450016],\n", - " [0.69038878, 0.0930877 ],\n", - " [0.69409156, 0.09343126],\n", - " [0.69779433, 0.0932404 ],\n", - " [0.70149709, 0.09350762],\n", - " [0.70519988, 0.09339309],\n", - " [0.70890264, 0.09291591],\n", - " [0.7126054 , 0.09303043],\n", - " [0.71630818, 0.0926296 ],\n", - " [0.72001095, 0.0932404 ],\n", - " [0.72371371, 0.09261052],\n", - " [0.72741648, 0.09249599],\n", - " [0.73111925, 0.09240055],\n", - " [0.73482204, 0.09253416],\n", - " [0.7385248 , 0.09209515],\n", - " [0.74222757, 0.09234329],\n", - " [0.74593034, 0.09366032],\n", - " [0.74963312, 0.09333583],\n", - " [0.75333589, 0.09322131],\n", - " [0.75703868, 0.09264868],\n", - " [0.76074146, 0.09253416],\n", - " [0.76444422, 0.09243873],\n", - " [0.76814698, 0.09230512],\n", - " [0.77184976, 0.09310678],\n", - " [0.77555253, 0.09165615],\n", - " [0.77925531, 0.09159888],\n", - " [0.78295807, 0.09207606],\n", - " [0.78666085, 0.09175158],\n", - " [0.79036364, 0.09177067],\n", - " [0.79406641, 0.09236237],\n", - " [0.79776918, 0.09241964],\n", - " [0.80147197, 0.09320222],\n", - " [0.80517474, 0.09199972],\n", - " [0.80887751, 0.09167523],\n", - " [0.81258028, 0.09322131],\n", - " [0.81628304, 0.09190428],\n", - " [0.81998581, 0.09167523],\n", - " [0.82368858, 0.09285865],\n", - " [0.82739136, 0.09180884],\n", - " [0.83109411, 0.09150345],\n", - " [0.83479688, 0.09186611],\n", - " [0.83849965, 0.0920188 ],\n", - " [0.84220242, 0.09320222],\n", - " [0.84590519, 0.09131257],\n", - " [0.84960797, 0.09117896],\n", - " [0.85331075, 0.09133166],\n", - " [0.85701353, 0.09089265],\n", - " [0.86071631, 0.09058725],\n", - " [0.86441907, 0.09051091],\n", - " [0.86812186, 0.09033912],\n", - " [0.87182464, 0.09041547],\n", - " [0.87552742, 0.0911217 ],\n", - " [0.87923019, 0.0894611 ],\n", - " [0.88293296, 0.08999555],\n", - " [0.88663573, 0.08921297],\n", - " [0.89033849, 0.08881213],\n", - " [0.89404126, 0.08797229],\n", - " [0.89774404, 0.08709427],\n", - " [0.9014468 , 0.08503284],\n", - " [1. , 0.07601531]])\n", - "\n", - "pos_ocp = np.array([[0.24879728, 4.4 ],\n", - " [0.26614516, 4.2935653 ],\n", - " [0.26886763, 4.2768621 ],\n", - " [0.27159011, 4.2647018 ],\n", - " [0.27431258, 4.2540312 ],\n", - " [0.27703505, 4.2449446 ],\n", - " [0.27975753, 4.2364879 ],\n", - " [0.28248 , 4.2302647 ],\n", - " [0.28520247, 4.2225528 ],\n", - " [0.28792495, 4.2182574 ],\n", - " [0.29064743, 4.213294 ],\n", - " [0.29336992, 4.2090373 ],\n", - " [0.29609239, 4.2051239 ],\n", - " [0.29881487, 4.2012677 ],\n", - " [0.30153735, 4.1981564 ],\n", - " [0.30425983, 4.1955218 ],\n", - " [0.30698231, 4.1931167 ],\n", - " [0.30970478, 4.1889744 ],\n", - " [0.31242725, 4.1881533 ],\n", - " [0.31514973, 4.1865883 ],\n", - " [0.3178722 , 4.1850228 ],\n", - " [0.32059466, 4.1832285 ],\n", - " [0.32331714, 4.1808805 ],\n", - " [0.32603962, 4.1805749 ],\n", - " [0.32876209, 4.1789522 ],\n", - " [0.33148456, 4.1768146 ],\n", - " [0.33420703, 4.1768146 ],\n", - " [0.3369295 , 4.1752872 ],\n", - " [0.33965197, 4.173111 ],\n", - " [0.34237446, 4.1726718 ],\n", - " [0.34509694, 4.1710877 ],\n", - " [0.34781941, 4.1702285 ],\n", - " [0.3505419 , 4.168797 ],\n", - " [0.35326438, 4.1669831 ],\n", - " [0.35598685, 4.1655135 ],\n", - " [0.35870932, 4.1634517 ],\n", - " [0.3614318 , 4.1598248 ],\n", - " [0.36415428, 4.1571712 ],\n", - " [0.36687674, 4.154079 ],\n", - " [0.36959921, 4.1504135 ],\n", - " [0.37232169, 4.1466532 ],\n", - " [0.37504418, 4.1423388 ],\n", - " [0.37776665, 4.1382346 ],\n", - " [0.38048913, 4.1338248 ],\n", - " [0.38321161, 4.1305799 ],\n", - " [0.38593408, 4.1272392 ],\n", - " [0.38865655, 4.1228104 ],\n", - " [0.39137903, 4.1186109 ],\n", - " [0.39410151, 4.114182 ],\n", - " [0.39682398, 4.1096005 ],\n", - " [0.39954645, 4.1046948 ],\n", - " [0.40226892, 4.1004758 ],\n", - " [0.4049914 , 4.0956464 ],\n", - " [0.40771387, 4.0909696 ],\n", - " [0.41043634, 4.0864644 ],\n", - " [0.41315882, 4.0818448 ],\n", - " [0.41588129, 4.077683 ],\n", - " [0.41860377, 4.0733309 ],\n", - " [0.42132624, 4.0690737 ],\n", - " [0.42404872, 4.0647216 ],\n", - " [0.4267712 , 4.0608654 ],\n", - " [0.42949368, 4.0564747 ],\n", - " [0.43221616, 4.0527525 ],\n", - " [0.43493864, 4.0492401 ],\n", - " [0.43766111, 4.0450211 ],\n", - " [0.44038359, 4.041986 ],\n", - " [0.44310607, 4.0384736 ],\n", - " [0.44582856, 4.035171 ],\n", - " [0.44855103, 4.0320406 ],\n", - " [0.45127351, 4.0289288 ],\n", - " [0.453996 , 4.02597 ],\n", - " [0.45671848, 4.0227437 ],\n", - " [0.45944095, 4.0199757 ],\n", - " [0.46216343, 4.0175133 ],\n", - " [0.46488592, 4.0149746 ],\n", - " [0.46760838, 4.0122066 ],\n", - " [0.47033085, 4.009954 ],\n", - " [0.47305333, 4.0075679 ],\n", - " [0.47577581, 4.0050669 ],\n", - " [0.47849828, 4.0023184 ],\n", - " [0.48122074, 3.9995501 ],\n", - " [0.48394321, 3.9969349 ],\n", - " [0.48666569, 3.9926589 ],\n", - " [0.48938816, 3.9889555 ],\n", - " [0.49211064, 3.9834003 ],\n", - " [0.4948331 , 3.9783037 ],\n", - " [0.49755557, 3.9755929 ],\n", - " [0.50027804, 3.9707632 ],\n", - " [0.50300052, 3.9681098 ],\n", - " [0.50572298, 3.9635665 ],\n", - " [0.50844545, 3.9594433 ],\n", - " [0.51116792, 3.9556634 ],\n", - " [0.51389038, 3.9521511 ],\n", - " [0.51661284, 3.9479132 ],\n", - " [0.51933531, 3.9438281 ],\n", - " [0.52205777, 3.9400866 ],\n", - " [0.52478024, 3.9362304 ],\n", - " [0.52750271, 3.9314201 ],\n", - " [0.53022518, 3.9283848 ],\n", - " [0.53294765, 3.9242232 ],\n", - " [0.53567012, 3.9192028 ],\n", - " [0.53839258, 3.9166257 ],\n", - " [0.54111506, 3.9117961 ],\n", - " [0.54383753, 3.90815 ],\n", - " [0.54656 , 3.9038739 ],\n", - " [0.54928247, 3.8995597 ],\n", - " [0.55200494, 3.8959136 ],\n", - " [0.5547274 , 3.8909314 ],\n", - " [0.55744986, 3.8872662 ],\n", - " [0.56017233, 3.8831048 ],\n", - " [0.5628948 , 3.8793442 ],\n", - " [0.56561729, 3.8747628 ],\n", - " [0.56833976, 3.8702576 ],\n", - " [0.57106222, 3.8666878 ],\n", - " [0.57378469, 3.8623927 ],\n", - " [0.57650716, 3.8581741 ],\n", - " [0.57922963, 3.854146 ],\n", - " [0.5819521 , 3.8499846 ],\n", - " [0.58467456, 3.8450022 ],\n", - " [0.58739702, 3.8422534 ],\n", - " [0.59011948, 3.8380919 ],\n", - " [0.59284194, 3.8341596 ],\n", - " [0.5955644 , 3.8309333 ],\n", - " [0.59828687, 3.8272109 ],\n", - " [0.60100935, 3.823164 ],\n", - " [0.60373182, 3.8192315 ],\n", - " [0.60645429, 3.8159864 ],\n", - " [0.60917677, 3.8123021 ],\n", - " [0.61189925, 3.8090379 ],\n", - " [0.61462172, 3.8071671 ],\n", - " [0.61734419, 3.8040555 ],\n", - " [0.62006666, 3.8013639 ],\n", - " [0.62278914, 3.7970879 ],\n", - " [0.62551162, 3.7953317 ],\n", - " [0.62823408, 3.7920673 ],\n", - " [0.63095656, 3.788383 ],\n", - " [0.63367903, 3.7855389 ],\n", - " [0.6364015 , 3.7838206 ],\n", - " [0.63912397, 3.78111 ],\n", - " [0.64184645, 3.7794874 ],\n", - " [0.64456893, 3.7769294 ],\n", - " [0.6472914 , 3.773608 ],\n", - " [0.65001389, 3.7695992 ],\n", - " [0.65273637, 3.7690265 ],\n", - " [0.65545884, 3.7662776 ],\n", - " [0.65818131, 3.7642922 ],\n", - " [0.66090379, 3.7626889 ],\n", - " [0.66362625, 3.7603791 ],\n", - " [0.66634874, 3.7575538 ],\n", - " [0.66907121, 3.7552056 ],\n", - " [0.67179369, 3.7533159 ],\n", - " [0.67451616, 3.7507198 ],\n", - " [0.67723865, 3.7487535 ],\n", - " [0.67996113, 3.7471499 ],\n", - " [0.68268361, 3.7442865 ],\n", - " [0.68540608, 3.7423012 ],\n", - " [0.68812855, 3.7400677 ],\n", - " [0.69085103, 3.7385788 ],\n", - " [0.6935735 , 3.7345319 ],\n", - " [0.69629597, 3.7339211 ],\n", - " [0.69901843, 3.7301605 ],\n", - " [0.7017409 , 3.7301033 ],\n", - " [0.70446338, 3.7278316 ],\n", - " [0.70718585, 3.7251589 ],\n", - " [0.70990833, 3.723861 ],\n", - " [0.71263081, 3.7215703 ],\n", - " [0.71535328, 3.7191267 ],\n", - " [0.71807574, 3.7172751 ],\n", - " [0.72079822, 3.7157097 ],\n", - " [0.72352069, 3.7130945 ],\n", - " [0.72624317, 3.7099447 ],\n", - " [0.72896564, 3.7071004 ],\n", - " [0.7316881 , 3.7045615 ],\n", - " [0.73441057, 3.703588 ],\n", - " [0.73713303, 3.70208 ],\n", - " [0.73985551, 3.7002664 ],\n", - " [0.74257799, 3.6972122 ],\n", - " [0.74530047, 3.6952841 ],\n", - " [0.74802293, 3.6929362 ],\n", - " [0.7507454 , 3.6898055 ],\n", - " [0.75346787, 3.6890991 ],\n", - " [0.75619034, 3.686522 ],\n", - " [0.75891281, 3.6849759 ],\n", - " [0.76163529, 3.6821697 ],\n", - " [0.76435776, 3.6808143 ],\n", - " [0.76708024, 3.6786573 ],\n", - " [0.7698027 , 3.6761947 ],\n", - " [0.77252517, 3.674763 ],\n", - " [0.77524765, 3.6712887 ],\n", - " [0.77797012, 3.6697233 ],\n", - " [0.78069258, 3.6678908 ],\n", - " [0.78341506, 3.6652565 ],\n", - " [0.78613753, 3.6630611 ],\n", - " [0.78885999, 3.660274 ],\n", - " [0.79158246, 3.6583652 ],\n", - " [0.79430494, 3.6554828 ],\n", - " [0.79702741, 3.6522949 ],\n", - " [0.79974987, 3.6499848 ],\n", - " [0.80247234, 3.6470451 ],\n", - " [0.8051948 , 3.6405547 ],\n", - " [0.80791727, 3.6383405 ],\n", - " [0.81063974, 3.635076 ],\n", - " [0.81336221, 3.633549 ],\n", - " [0.81608468, 3.6322317 ],\n", - " [0.81880714, 3.6306856 ],\n", - " [0.82152961, 3.6283948 ],\n", - " [0.82425208, 3.6268487 ],\n", - " [0.82697453, 3.6243098 ],\n", - " [0.829697 , 3.6223626 ],\n", - " [0.83241946, 3.6193655 ],\n", - " [0.83514192, 3.6177621 ],\n", - " [0.83786439, 3.6158531 ],\n", - " [0.84058684, 3.6128371 ],\n", - " [0.84330931, 3.6118062 ],\n", - " [0.84603177, 3.6094582 ],\n", - " [0.84875424, 3.6072438 ],\n", - " [0.8514767 , 3.6049912 ],\n", - " [0.85419916, 3.6030822 ],\n", - " [0.85692162, 3.6012688 ],\n", - " [0.85964409, 3.5995889 ],\n", - " [0.86236656, 3.5976417 ],\n", - " [0.86508902, 3.5951984 ],\n", - " [0.86781149, 3.593843 ],\n", - " [0.87053395, 3.5916286 ],\n", - " [0.87325642, 3.5894907 ],\n", - " [0.87597888, 3.587429 ],\n", - " [0.87870135, 3.5852909 ],\n", - " [0.88142383, 3.5834775 ],\n", - " [0.8841463 , 3.5817785 ],\n", - " [0.88686877, 3.5801177 ],\n", - " [0.88959124, 3.5778842 ],\n", - " [0.89231371, 3.5763381 ],\n", - " [0.8950362 , 3.5737801 ],\n", - " [0.89775868, 3.5721002 ],\n", - " [0.90048116, 3.5702102 ],\n", - " [0.90320364, 3.5684922 ],\n", - " [0.90592613, 3.5672133 ],\n", - " [1. , 3.52302167]])\n", - "\n", - "from pybamm import exp, constants\n", - "\n", - "\n", - "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", - " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 35000\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", - " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 17800\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "\n", - "values = {\n", - " 'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Typical current [A]': 5.0,\n", - " 'Current function [A]': 5.0,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Upper voltage cut-off [V]': 4.4,\n", - " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15\n", - "}\n", - "param = pybamm.ParameterValues(values)\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we would have got the same result by doing" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Current function [A]': 5.0,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode OCP [V]': ,\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode OCP [V]': ,\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Upper voltage cut-off [V]': 4.2,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_same = pybamm.ParameterValues(\"Chen2020\")\n", - "{k: v for k,v in param_same.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating a specific parameter" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a constant value:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current function [A]\t5.0\n" - ] - }, - { - "data": { - "text/plain": [ - "4.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.search(\"Current function [A]\")\n", - "\n", - "param.update({\"Current function [A]\": 4.0})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a function:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def curren_func(time):\n", - " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", - "\n", - "param.update({\"Current function [A]\": curren_func})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting parameter functions\n", - "\n", - "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting \"Current function \\[A]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "currentfunc = param[\"Current function [A]\"]\n", - "time = pybamm.linspace(0, 120, 60)\n", - "evaluated = param.evaluate(currentfunc(time))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(time, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking another such example:\n", - "\n", - "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", - "x = pybamm.linspace(3000,6000,100)\n", - "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", - "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(x, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating and solving the model\n", - "\n", - "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" - ] - } - ], - "source": [ - "sim = pybamm.Simulation(spm, parameter_values=param)\n", - "t_eval = np.arange(0, 3600, 1)\n", - "sim.solve(t_eval=t_eval)\n", - "sim.plot()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[3] Charles R. Harris, K. Jarrod Millman, StΓ©fan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "\n" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "vscode": { - "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameterisation\n", + "\n", + "In this notebook, we show how to find which parameters are needed in a model and define them.\n", + "\n", + "For other notebooks about parameterization, see:\n", + "\n", + "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n", + "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", + "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding your own parameter sets (using a dictionary)\n", + "\n", + "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the model\n", + "\n", + "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", + "\n", + "R = pybamm.Parameter(\"Particle radius [m]\")\n", + "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", + "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", + "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", + "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.BaseModel()\n", + "\n", + "# governing equations\n", + "N = -D * pybamm.grad(c) # flux\n", + "dcdt = -pybamm.div(N)\n", + "model.rhs = {c: dcdt} \n", + "\n", + "# boundary conditions \n", + "lbc = pybamm.Scalar(0)\n", + "rbc = -j\n", + "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", + "\n", + "# initial conditions \n", + "model.initial_conditions = {c: c0}\n", + "\n", + "model.variables = {\n", + " \"Concentration [mol.m-3]\": c,\n", + " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", + " \"Flux [mol.m-2.s-1]\": N,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also define the geometry, since there are parameters in the geometry too" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters required" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To know what parameters are required by the model and geometry, we can do" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial concentration [mol.m-3] (Parameter)\n", + "Interfacial current density [A.m-2] (InputParameter)\n", + "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n", + "\n", + "Particle radius [m] (Parameter)\n" + ] + } + ], + "source": [ + "model.print_parameter_info()\n", + "geometry.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding the parameters\n", + "\n", + "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def D_fun(c):\n", + " return 3.9 #* pybamm.exp(-c)\n", + "\n", + "\n", + "values = {\n", + " \"Particle radius [m]\": 2,\n", + " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", + " \"Initial concentration [mol.m-3]\": 2.5,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n", + " 'Diffusion coefficient [m2.s-1]': ,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Initial concentration [mol.m-3]': 2.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param = pybamm.ParameterValues(values)\n", + "\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating the parameter values\n", + "\n", + "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n", + " 'Diffusion coefficient [m2.s-1]': ,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Initial concentration [mol.m-3]': 1.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the model " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n", + " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n", + " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters = model.parameters\n", + "parameters" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "param.process_model(model)\n", + "param.process_geometry(geometry)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now set up our mesh, choose a spatial method, and discretise our model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", + "var_pts = {r: 20}\n", + "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", + "\n", + "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", + "disc = pybamm.Discretisation(mesh, spatial_methods)\n", + "disc.process_model(model);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# solve\n", + "solver = pybamm.ScipySolver()\n", + "t = np.linspace(0, 3600, 600)\n", + "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", + "\n", + "# post-process, so that the solution can be called at any time t or space r\n", + "# (using interpolation)\n", + "c = solution[\"Concentration [mol.m-3]\"]\n", + "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", + "\n", + "# plot\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", + "\n", + "ax1.plot(solution.t, c_surf(solution.t))\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", + "\n", + "rsol = mesh[\"negative particle\"].nodes # radial position\n", + "time = 1000 # time in seconds\n", + "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", + "ax2.set_xlabel(\"Particle radius [microns]\")\n", + "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", + "ax2.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using pre-defined models in `PyBaMM`\n", + "\n", + "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", + "\n", + "We start by initializing our model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "spm = pybamm.lithium_ion.SPM()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "We can print the `parameters` of a model by using the `get_parameters_info` function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Lower voltage cut-off [V] (Parameter)\n", + "Faraday constant [C.mol-1] (Parameter)\n", + "Ideal gas constant [J.K-1.mol-1] (Parameter)\n", + "Electrode width [m] (Parameter)\n", + "Positive electrode thickness [m] (Parameter)\n", + "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Upper voltage cut-off [V] (Parameter)\n", + "Number of electrodes connected in parallel to make a cell (Parameter)\n", + "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", + "Nominal cell capacity [A.h] (Parameter)\n", + "Reference temperature [K] (Parameter)\n", + "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", + "Separator thickness [m] (Parameter)\n", + "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Electrode height [m] (Parameter)\n", + "Number of cells connected in series to make a battery (Parameter)\n", + "Negative electrode thickness [m] (Parameter)\n", + "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n", + "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", + "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n", + "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n", + "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", + "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", + "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", + "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", + "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", + "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", + "\n" + ] + } + ], + "source": [ + "spm.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Negative electrode thickness [m]': 0.0001,\n", + " 'Separator thickness [m]': 2.5e-05,\n", + " 'Positive electrode thickness [m]': 0.0001,\n", + " 'Electrode height [m]': 0.137,\n", + " 'Electrode width [m]': 0.207,\n", + " 'Nominal cell capacity [A.h]': 0.680616,\n", + " 'Current function [A]': 0.680616,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", + " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode porosity': 0.3,\n", + " 'Negative electrode active material volume fraction': 0.6,\n", + " 'Negative particle radius [m]': 1e-05,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': ,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", + " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode porosity': 0.3,\n", + " 'Positive electrode active material volume fraction': 0.5,\n", + " 'Positive particle radius [m]': 1e-05,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': ,\n", + " 'Separator porosity': 1.0,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 3.105,\n", + " 'Upper voltage cut-off [V]': 4.1,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ambient temperature [K]': 298.15,\n", + " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n", + " 'Current function [A]': 5.0,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", + " 'Initial temperature [K]': 298.15,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n", + " ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n", + " 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n", + " 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n", + " 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n", + " 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n", + " 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n", + " 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n", + " 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n", + " 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n", + " 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n", + " 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n", + " 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n", + " 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n", + " 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n", + " 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n", + " 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n", + " 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n", + " 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n", + " 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n", + " 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n", + " 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n", + " 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n", + " 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n", + " 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n", + " 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n", + " 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n", + " 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n", + " 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n", + " 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n", + " 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n", + " 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n", + " 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n", + " 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n", + " 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n", + " 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n", + " 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n", + " 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n", + " 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n", + " 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n", + " 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n", + " 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n", + " 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n", + " 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n", + " 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n", + " 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n", + " 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n", + " 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n", + " 0.89774404, 0.9014468 , 1. ])],\n", + " array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n", + " 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n", + " 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n", + " 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n", + " 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n", + " 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n", + " 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n", + " 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n", + " 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n", + " 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n", + " 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n", + " 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n", + " 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n", + " 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n", + " 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n", + " 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n", + " 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n", + " 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n", + " 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n", + " 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n", + " 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n", + " 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n", + " 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n", + " 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n", + " 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n", + " 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n", + " 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n", + " 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n", + " 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n", + " 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n", + " 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n", + " 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n", + " 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n", + " 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n", + " 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n", + " 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n", + " 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n", + " 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n", + " 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n", + " 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n", + " 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n", + " 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n", + " 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n", + " 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n", + " 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n", + " 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n", + " 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n", + " 0.08709427, 0.08503284, 0.07601531]))),\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n", + " ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n", + " 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n", + " 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n", + " 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n", + " 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n", + " 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n", + " 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n", + " 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n", + " 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n", + " 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n", + " 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n", + " 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n", + " 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n", + " 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n", + " 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n", + " 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n", + " 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n", + " 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n", + " 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n", + " 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n", + " 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n", + " 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n", + " 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n", + " 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n", + " 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n", + " 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n", + " 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n", + " 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n", + " 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n", + " 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n", + " 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n", + " 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n", + " 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n", + " 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n", + " 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n", + " 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n", + " 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n", + " 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n", + " 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n", + " 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n", + " 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n", + " 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n", + " 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n", + " 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n", + " 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n", + " 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n", + " 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n", + " 0.90320364, 0.90592613, 1. ])],\n", + " array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n", + " 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n", + " 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n", + " 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n", + " 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n", + " 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n", + " 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n", + " 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n", + " 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n", + " 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n", + " 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n", + " 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n", + " 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n", + " 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n", + " 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n", + " 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n", + " 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n", + " 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n", + " 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n", + " 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n", + " 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n", + " 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n", + " 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n", + " 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n", + " 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n", + " 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n", + " 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n", + " 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n", + " 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n", + " 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n", + " 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n", + " 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n", + " 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n", + " 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n", + " 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n", + " 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n", + " 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n", + " 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n", + " 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n", + " 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n", + " 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n", + " 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n", + " 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n", + " 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n", + " 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n", + " 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n", + " 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n", + " 3.5684922 , 3.5672133 , 3.52302167]))),\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Typical current [A]': 5.0,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Upper voltage cut-off [V]': 4.4}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", + " D_ref = 3.9 * 10 ** (-14)\n", + " E_D_s = 42770\n", + " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", + " return D_ref * arrhenius\n", + "\n", + "\n", + "neg_ocp = np.array([[0. , 1.81772748],\n", + " [0.03129623, 1.0828807 ],\n", + " [0.03499902, 0.99593794],\n", + " [0.0387018 , 0.90023398],\n", + " [0.04240458, 0.79649431],\n", + " [0.04610736, 0.73354429],\n", + " [0.04981015, 0.66664314],\n", + " [0.05351292, 0.64137149],\n", + " [0.05721568, 0.59813869],\n", + " [0.06091845, 0.5670836 ],\n", + " [0.06462122, 0.54746181],\n", + " [0.06832399, 0.53068399],\n", + " [0.07202675, 0.51304734],\n", + " [0.07572951, 0.49394092],\n", + " [0.07943227, 0.47926274],\n", + " [0.08313503, 0.46065259],\n", + " [0.08683779, 0.45992726],\n", + " [0.09054054, 0.43801501],\n", + " [0.09424331, 0.42438665],\n", + " [0.09794607, 0.41150269],\n", + " [0.10164883, 0.40033659],\n", + " [0.10535158, 0.38957134],\n", + " [0.10905434, 0.37756538],\n", + " [0.1127571 , 0.36292541],\n", + " [0.11645985, 0.34357086],\n", + " [0.12016261, 0.3406314 ],\n", + " [0.12386536, 0.32299468],\n", + " [0.12756811, 0.31379458],\n", + " [0.13127086, 0.30795386],\n", + " [0.13497362, 0.29207319],\n", + " [0.13867638, 0.28697687],\n", + " [0.14237913, 0.27405477],\n", + " [0.14608189, 0.2670497 ],\n", + " [0.14978465, 0.25857493],\n", + " [0.15348741, 0.25265783],\n", + " [0.15719018, 0.24826777],\n", + " [0.16089294, 0.2414345 ],\n", + " [0.1645957 , 0.23362778],\n", + " [0.16829847, 0.22956218],\n", + " [0.17200122, 0.22370236],\n", + " [0.17570399, 0.22181271],\n", + " [0.17940674, 0.22089651],\n", + " [0.1831095 , 0.2194268 ],\n", + " [0.18681229, 0.21830064],\n", + " [0.19051504, 0.21845333],\n", + " [0.1942178 , 0.21753715],\n", + " [0.19792056, 0.21719357],\n", + " [0.20162334, 0.21635373],\n", + " [0.2053261 , 0.21667822],\n", + " [0.20902886, 0.21738444],\n", + " [0.21273164, 0.21469313],\n", + " [0.2164344 , 0.21541846],\n", + " [0.22013716, 0.21465495],\n", + " [0.22383993, 0.2135479 ],\n", + " [0.2275427 , 0.21392964],\n", + " [0.23124547, 0.21074206],\n", + " [0.23494825, 0.20873788],\n", + " [0.23865101, 0.20465319],\n", + " [0.24235377, 0.20205732],\n", + " [0.24605653, 0.19774358],\n", + " [0.2497593 , 0.19444147],\n", + " [0.25346208, 0.19190285],\n", + " [0.25716486, 0.18850531],\n", + " [0.26086762, 0.18581399],\n", + " [0.26457039, 0.18327537],\n", + " [0.26827314, 0.18157659],\n", + " [0.2719759 , 0.17814088],\n", + " [0.27567867, 0.17529686],\n", + " [0.27938144, 0.1719375 ],\n", + " [0.28308421, 0.16934161],\n", + " [0.28678698, 0.16756649],\n", + " [0.29048974, 0.16609676],\n", + " [0.29419251, 0.16414985],\n", + " [0.29789529, 0.16260378],\n", + " [0.30159806, 0.16224113],\n", + " [0.30530083, 0.160027 ],\n", + " [0.30900361, 0.15827096],\n", + " [0.31270637, 0.1588054 ],\n", + " [0.31640913, 0.15552238],\n", + " [0.32011189, 0.15580869],\n", + " [0.32381466, 0.15220118],\n", + " [0.32751744, 0.1511132 ],\n", + " [0.33122021, 0.14987253],\n", + " [0.33492297, 0.14874637],\n", + " [0.33862575, 0.14678037],\n", + " [0.34232853, 0.14620776],\n", + " [0.34603131, 0.14555879],\n", + " [0.34973408, 0.14389819],\n", + " [0.35343685, 0.14359279],\n", + " [0.35713963, 0.14242846],\n", + " [0.36084241, 0.14038612],\n", + " [0.36454517, 0.13882096],\n", + " [0.36824795, 0.13954628],\n", + " [0.37195071, 0.13946992],\n", + " [0.37565348, 0.13780934],\n", + " [0.37935626, 0.13973714],\n", + " [0.38305904, 0.13698858],\n", + " [0.38676182, 0.13523254],\n", + " [0.3904646 , 0.13441178],\n", + " [0.39416737, 0.1352898 ],\n", + " [0.39787015, 0.13507985],\n", + " [0.40157291, 0.13647321],\n", + " [0.40527567, 0.13601512],\n", + " [0.40897844, 0.13435452],\n", + " [0.41268121, 0.1334765 ],\n", + " [0.41638398, 0.1348317 ],\n", + " [0.42008676, 0.13275118],\n", + " [0.42378953, 0.13286571],\n", + " [0.4274923 , 0.13263667],\n", + " [0.43119506, 0.13456447],\n", + " [0.43489784, 0.13471718],\n", + " [0.43860061, 0.13395369],\n", + " [0.44230338, 0.13448814],\n", + " [0.44600615, 0.1334765 ],\n", + " [0.44970893, 0.13298023],\n", + " [0.45341168, 0.13259849],\n", + " [0.45711444, 0.13338107],\n", + " [0.46081719, 0.13309476],\n", + " [0.46451994, 0.13275118],\n", + " [0.46822269, 0.13443087],\n", + " [0.47192545, 0.13315202],\n", + " [0.47562821, 0.132713 ],\n", + " [0.47933098, 0.1330184 ],\n", + " [0.48303375, 0.13278936],\n", + " [0.48673651, 0.13225491],\n", + " [0.49043926, 0.13317111],\n", + " [0.49414203, 0.13263667],\n", + " [0.49784482, 0.13187316],\n", + " [0.50154759, 0.13265574],\n", + " [0.50525036, 0.13250305],\n", + " [0.50895311, 0.13324745],\n", + " [0.51265586, 0.13204496],\n", + " [0.51635861, 0.13242669],\n", + " [0.52006139, 0.13233127],\n", + " [0.52376415, 0.13198769],\n", + " [0.52746692, 0.13254122],\n", + " [0.53116969, 0.13145325],\n", + " [0.53487245, 0.13298023],\n", + " [0.53857521, 0.13168229],\n", + " [0.54227797, 0.1313578 ],\n", + " [0.54598074, 0.13235036],\n", + " [0.5496835 , 0.13120511],\n", + " [0.55338627, 0.13089971],\n", + " [0.55708902, 0.13109058],\n", + " [0.56079178, 0.13082336],\n", + " [0.56449454, 0.13011713],\n", + " [0.5681973 , 0.129869 ],\n", + " [0.57190006, 0.12992626],\n", + " [0.57560282, 0.12942998],\n", + " [0.57930558, 0.12796026],\n", + " [0.58300835, 0.12862831],\n", + " [0.58671112, 0.12656689],\n", + " [0.59041389, 0.12734947],\n", + " [0.59411664, 0.12509716],\n", + " [0.59781941, 0.12110791],\n", + " [0.60152218, 0.11839751],\n", + " [0.60522496, 0.11244226],\n", + " [0.60892772, 0.11307214],\n", + " [0.61263048, 0.1092165 ],\n", + " [0.61633325, 0.10683058],\n", + " [0.62003603, 0.10433014],\n", + " [0.6237388 , 0.10530359],\n", + " [0.62744156, 0.10056993],\n", + " [0.63114433, 0.09950104],\n", + " [0.63484711, 0.09854668],\n", + " [0.63854988, 0.09921473],\n", + " [0.64225265, 0.09541635],\n", + " [0.64595543, 0.09980643],\n", + " [0.64965823, 0.0986612 ],\n", + " [0.653361 , 0.09560722],\n", + " [0.65706377, 0.09755413],\n", + " [0.66076656, 0.09612258],\n", + " [0.66446934, 0.09430929],\n", + " [0.66817212, 0.09661885],\n", + " [0.67187489, 0.09366032],\n", + " [0.67557767, 0.09522548],\n", + " [0.67928044, 0.09535909],\n", + " [0.68298322, 0.09316404],\n", + " [0.686686 , 0.09450016],\n", + " [0.69038878, 0.0930877 ],\n", + " [0.69409156, 0.09343126],\n", + " [0.69779433, 0.0932404 ],\n", + " [0.70149709, 0.09350762],\n", + " [0.70519988, 0.09339309],\n", + " [0.70890264, 0.09291591],\n", + " [0.7126054 , 0.09303043],\n", + " [0.71630818, 0.0926296 ],\n", + " [0.72001095, 0.0932404 ],\n", + " [0.72371371, 0.09261052],\n", + " [0.72741648, 0.09249599],\n", + " [0.73111925, 0.09240055],\n", + " [0.73482204, 0.09253416],\n", + " [0.7385248 , 0.09209515],\n", + " [0.74222757, 0.09234329],\n", + " [0.74593034, 0.09366032],\n", + " [0.74963312, 0.09333583],\n", + " [0.75333589, 0.09322131],\n", + " [0.75703868, 0.09264868],\n", + " [0.76074146, 0.09253416],\n", + " [0.76444422, 0.09243873],\n", + " [0.76814698, 0.09230512],\n", + " [0.77184976, 0.09310678],\n", + " [0.77555253, 0.09165615],\n", + " [0.77925531, 0.09159888],\n", + " [0.78295807, 0.09207606],\n", + " [0.78666085, 0.09175158],\n", + " [0.79036364, 0.09177067],\n", + " [0.79406641, 0.09236237],\n", + " [0.79776918, 0.09241964],\n", + " [0.80147197, 0.09320222],\n", + " [0.80517474, 0.09199972],\n", + " [0.80887751, 0.09167523],\n", + " [0.81258028, 0.09322131],\n", + " [0.81628304, 0.09190428],\n", + " [0.81998581, 0.09167523],\n", + " [0.82368858, 0.09285865],\n", + " [0.82739136, 0.09180884],\n", + " [0.83109411, 0.09150345],\n", + " [0.83479688, 0.09186611],\n", + " [0.83849965, 0.0920188 ],\n", + " [0.84220242, 0.09320222],\n", + " [0.84590519, 0.09131257],\n", + " [0.84960797, 0.09117896],\n", + " [0.85331075, 0.09133166],\n", + " [0.85701353, 0.09089265],\n", + " [0.86071631, 0.09058725],\n", + " [0.86441907, 0.09051091],\n", + " [0.86812186, 0.09033912],\n", + " [0.87182464, 0.09041547],\n", + " [0.87552742, 0.0911217 ],\n", + " [0.87923019, 0.0894611 ],\n", + " [0.88293296, 0.08999555],\n", + " [0.88663573, 0.08921297],\n", + " [0.89033849, 0.08881213],\n", + " [0.89404126, 0.08797229],\n", + " [0.89774404, 0.08709427],\n", + " [0.9014468 , 0.08503284],\n", + " [1. , 0.07601531]])\n", + "\n", + "pos_ocp = np.array([[0.24879728, 4.4 ],\n", + " [0.26614516, 4.2935653 ],\n", + " [0.26886763, 4.2768621 ],\n", + " [0.27159011, 4.2647018 ],\n", + " [0.27431258, 4.2540312 ],\n", + " [0.27703505, 4.2449446 ],\n", + " [0.27975753, 4.2364879 ],\n", + " [0.28248 , 4.2302647 ],\n", + " [0.28520247, 4.2225528 ],\n", + " [0.28792495, 4.2182574 ],\n", + " [0.29064743, 4.213294 ],\n", + " [0.29336992, 4.2090373 ],\n", + " [0.29609239, 4.2051239 ],\n", + " [0.29881487, 4.2012677 ],\n", + " [0.30153735, 4.1981564 ],\n", + " [0.30425983, 4.1955218 ],\n", + " [0.30698231, 4.1931167 ],\n", + " [0.30970478, 4.1889744 ],\n", + " [0.31242725, 4.1881533 ],\n", + " [0.31514973, 4.1865883 ],\n", + " [0.3178722 , 4.1850228 ],\n", + " [0.32059466, 4.1832285 ],\n", + " [0.32331714, 4.1808805 ],\n", + " [0.32603962, 4.1805749 ],\n", + " [0.32876209, 4.1789522 ],\n", + " [0.33148456, 4.1768146 ],\n", + " [0.33420703, 4.1768146 ],\n", + " [0.3369295 , 4.1752872 ],\n", + " [0.33965197, 4.173111 ],\n", + " [0.34237446, 4.1726718 ],\n", + " [0.34509694, 4.1710877 ],\n", + " [0.34781941, 4.1702285 ],\n", + " [0.3505419 , 4.168797 ],\n", + " [0.35326438, 4.1669831 ],\n", + " [0.35598685, 4.1655135 ],\n", + " [0.35870932, 4.1634517 ],\n", + " [0.3614318 , 4.1598248 ],\n", + " [0.36415428, 4.1571712 ],\n", + " [0.36687674, 4.154079 ],\n", + " [0.36959921, 4.1504135 ],\n", + " [0.37232169, 4.1466532 ],\n", + " [0.37504418, 4.1423388 ],\n", + " [0.37776665, 4.1382346 ],\n", + " [0.38048913, 4.1338248 ],\n", + " [0.38321161, 4.1305799 ],\n", + " [0.38593408, 4.1272392 ],\n", + " [0.38865655, 4.1228104 ],\n", + " [0.39137903, 4.1186109 ],\n", + " [0.39410151, 4.114182 ],\n", + " [0.39682398, 4.1096005 ],\n", + " [0.39954645, 4.1046948 ],\n", + " [0.40226892, 4.1004758 ],\n", + " [0.4049914 , 4.0956464 ],\n", + " [0.40771387, 4.0909696 ],\n", + " [0.41043634, 4.0864644 ],\n", + " [0.41315882, 4.0818448 ],\n", + " [0.41588129, 4.077683 ],\n", + " [0.41860377, 4.0733309 ],\n", + " [0.42132624, 4.0690737 ],\n", + " [0.42404872, 4.0647216 ],\n", + " [0.4267712 , 4.0608654 ],\n", + " [0.42949368, 4.0564747 ],\n", + " [0.43221616, 4.0527525 ],\n", + " [0.43493864, 4.0492401 ],\n", + " [0.43766111, 4.0450211 ],\n", + " [0.44038359, 4.041986 ],\n", + " [0.44310607, 4.0384736 ],\n", + " [0.44582856, 4.035171 ],\n", + " [0.44855103, 4.0320406 ],\n", + " [0.45127351, 4.0289288 ],\n", + " [0.453996 , 4.02597 ],\n", + " [0.45671848, 4.0227437 ],\n", + " [0.45944095, 4.0199757 ],\n", + " [0.46216343, 4.0175133 ],\n", + " [0.46488592, 4.0149746 ],\n", + " [0.46760838, 4.0122066 ],\n", + " [0.47033085, 4.009954 ],\n", + " [0.47305333, 4.0075679 ],\n", + " [0.47577581, 4.0050669 ],\n", + " [0.47849828, 4.0023184 ],\n", + " [0.48122074, 3.9995501 ],\n", + " [0.48394321, 3.9969349 ],\n", + " [0.48666569, 3.9926589 ],\n", + " [0.48938816, 3.9889555 ],\n", + " [0.49211064, 3.9834003 ],\n", + " [0.4948331 , 3.9783037 ],\n", + " [0.49755557, 3.9755929 ],\n", + " [0.50027804, 3.9707632 ],\n", + " [0.50300052, 3.9681098 ],\n", + " [0.50572298, 3.9635665 ],\n", + " [0.50844545, 3.9594433 ],\n", + " [0.51116792, 3.9556634 ],\n", + " [0.51389038, 3.9521511 ],\n", + " [0.51661284, 3.9479132 ],\n", + " [0.51933531, 3.9438281 ],\n", + " [0.52205777, 3.9400866 ],\n", + " [0.52478024, 3.9362304 ],\n", + " [0.52750271, 3.9314201 ],\n", + " [0.53022518, 3.9283848 ],\n", + " [0.53294765, 3.9242232 ],\n", + " [0.53567012, 3.9192028 ],\n", + " [0.53839258, 3.9166257 ],\n", + " [0.54111506, 3.9117961 ],\n", + " [0.54383753, 3.90815 ],\n", + " [0.54656 , 3.9038739 ],\n", + " [0.54928247, 3.8995597 ],\n", + " [0.55200494, 3.8959136 ],\n", + " [0.5547274 , 3.8909314 ],\n", + " [0.55744986, 3.8872662 ],\n", + " [0.56017233, 3.8831048 ],\n", + " [0.5628948 , 3.8793442 ],\n", + " [0.56561729, 3.8747628 ],\n", + " [0.56833976, 3.8702576 ],\n", + " [0.57106222, 3.8666878 ],\n", + " [0.57378469, 3.8623927 ],\n", + " [0.57650716, 3.8581741 ],\n", + " [0.57922963, 3.854146 ],\n", + " [0.5819521 , 3.8499846 ],\n", + " [0.58467456, 3.8450022 ],\n", + " [0.58739702, 3.8422534 ],\n", + " [0.59011948, 3.8380919 ],\n", + " [0.59284194, 3.8341596 ],\n", + " [0.5955644 , 3.8309333 ],\n", + " [0.59828687, 3.8272109 ],\n", + " [0.60100935, 3.823164 ],\n", + " [0.60373182, 3.8192315 ],\n", + " [0.60645429, 3.8159864 ],\n", + " [0.60917677, 3.8123021 ],\n", + " [0.61189925, 3.8090379 ],\n", + " [0.61462172, 3.8071671 ],\n", + " [0.61734419, 3.8040555 ],\n", + " [0.62006666, 3.8013639 ],\n", + " [0.62278914, 3.7970879 ],\n", + " [0.62551162, 3.7953317 ],\n", + " [0.62823408, 3.7920673 ],\n", + " [0.63095656, 3.788383 ],\n", + " [0.63367903, 3.7855389 ],\n", + " [0.6364015 , 3.7838206 ],\n", + " [0.63912397, 3.78111 ],\n", + " [0.64184645, 3.7794874 ],\n", + " [0.64456893, 3.7769294 ],\n", + " [0.6472914 , 3.773608 ],\n", + " [0.65001389, 3.7695992 ],\n", + " [0.65273637, 3.7690265 ],\n", + " [0.65545884, 3.7662776 ],\n", + " [0.65818131, 3.7642922 ],\n", + " [0.66090379, 3.7626889 ],\n", + " [0.66362625, 3.7603791 ],\n", + " [0.66634874, 3.7575538 ],\n", + " [0.66907121, 3.7552056 ],\n", + " [0.67179369, 3.7533159 ],\n", + " [0.67451616, 3.7507198 ],\n", + " [0.67723865, 3.7487535 ],\n", + " [0.67996113, 3.7471499 ],\n", + " [0.68268361, 3.7442865 ],\n", + " [0.68540608, 3.7423012 ],\n", + " [0.68812855, 3.7400677 ],\n", + " [0.69085103, 3.7385788 ],\n", + " [0.6935735 , 3.7345319 ],\n", + " [0.69629597, 3.7339211 ],\n", + " [0.69901843, 3.7301605 ],\n", + " [0.7017409 , 3.7301033 ],\n", + " [0.70446338, 3.7278316 ],\n", + " [0.70718585, 3.7251589 ],\n", + " [0.70990833, 3.723861 ],\n", + " [0.71263081, 3.7215703 ],\n", + " [0.71535328, 3.7191267 ],\n", + " [0.71807574, 3.7172751 ],\n", + " [0.72079822, 3.7157097 ],\n", + " [0.72352069, 3.7130945 ],\n", + " [0.72624317, 3.7099447 ],\n", + " [0.72896564, 3.7071004 ],\n", + " [0.7316881 , 3.7045615 ],\n", + " [0.73441057, 3.703588 ],\n", + " [0.73713303, 3.70208 ],\n", + " [0.73985551, 3.7002664 ],\n", + " [0.74257799, 3.6972122 ],\n", + " [0.74530047, 3.6952841 ],\n", + " [0.74802293, 3.6929362 ],\n", + " [0.7507454 , 3.6898055 ],\n", + " [0.75346787, 3.6890991 ],\n", + " [0.75619034, 3.686522 ],\n", + " [0.75891281, 3.6849759 ],\n", + " [0.76163529, 3.6821697 ],\n", + " [0.76435776, 3.6808143 ],\n", + " [0.76708024, 3.6786573 ],\n", + " [0.7698027 , 3.6761947 ],\n", + " [0.77252517, 3.674763 ],\n", + " [0.77524765, 3.6712887 ],\n", + " [0.77797012, 3.6697233 ],\n", + " [0.78069258, 3.6678908 ],\n", + " [0.78341506, 3.6652565 ],\n", + " [0.78613753, 3.6630611 ],\n", + " [0.78885999, 3.660274 ],\n", + " [0.79158246, 3.6583652 ],\n", + " [0.79430494, 3.6554828 ],\n", + " [0.79702741, 3.6522949 ],\n", + " [0.79974987, 3.6499848 ],\n", + " [0.80247234, 3.6470451 ],\n", + " [0.8051948 , 3.6405547 ],\n", + " [0.80791727, 3.6383405 ],\n", + " [0.81063974, 3.635076 ],\n", + " [0.81336221, 3.633549 ],\n", + " [0.81608468, 3.6322317 ],\n", + " [0.81880714, 3.6306856 ],\n", + " [0.82152961, 3.6283948 ],\n", + " [0.82425208, 3.6268487 ],\n", + " [0.82697453, 3.6243098 ],\n", + " [0.829697 , 3.6223626 ],\n", + " [0.83241946, 3.6193655 ],\n", + " [0.83514192, 3.6177621 ],\n", + " [0.83786439, 3.6158531 ],\n", + " [0.84058684, 3.6128371 ],\n", + " [0.84330931, 3.6118062 ],\n", + " [0.84603177, 3.6094582 ],\n", + " [0.84875424, 3.6072438 ],\n", + " [0.8514767 , 3.6049912 ],\n", + " [0.85419916, 3.6030822 ],\n", + " [0.85692162, 3.6012688 ],\n", + " [0.85964409, 3.5995889 ],\n", + " [0.86236656, 3.5976417 ],\n", + " [0.86508902, 3.5951984 ],\n", + " [0.86781149, 3.593843 ],\n", + " [0.87053395, 3.5916286 ],\n", + " [0.87325642, 3.5894907 ],\n", + " [0.87597888, 3.587429 ],\n", + " [0.87870135, 3.5852909 ],\n", + " [0.88142383, 3.5834775 ],\n", + " [0.8841463 , 3.5817785 ],\n", + " [0.88686877, 3.5801177 ],\n", + " [0.88959124, 3.5778842 ],\n", + " [0.89231371, 3.5763381 ],\n", + " [0.8950362 , 3.5737801 ],\n", + " [0.89775868, 3.5721002 ],\n", + " [0.90048116, 3.5702102 ],\n", + " [0.90320364, 3.5684922 ],\n", + " [0.90592613, 3.5672133 ],\n", + " [1. , 3.52302167]])\n", + "\n", + "from pybamm import exp, constants\n", + "\n", + "\n", + "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", + " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 35000\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", + " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 17800\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "values = {\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Typical current [A]': 5.0,\n", + " 'Current function [A]': 5.0,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Upper voltage cut-off [V]': 4.4,\n", + " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", + " 'Initial temperature [K]': 298.15\n", + "}\n", + "param = pybamm.ParameterValues(values)\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we would have got the same result by doing" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Current function [A]': 5.0,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Upper voltage cut-off [V]': 4.2,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_same = pybamm.ParameterValues(\"Chen2020\")\n", + "{k: v for k,v in param_same.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating a specific parameter" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a constant value:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current function [A]\t5.0\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.search(\"Current function [A]\")\n", + "\n", + "param.update({\"Current function [A]\": 4.0})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def curren_func(time):\n", + " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", + "\n", + "\n", + "param.update({\"Current function [A]\": curren_func})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting parameter functions\n", + "\n", + "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting \"Current function \\[A]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "currentfunc = param[\"Current function [A]\"]\n", + "time = pybamm.linspace(0, 120, 60)\n", + "evaluated = param.evaluate(currentfunc(time))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(time, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking another such example:\n", + "\n", + "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", + "x = pybamm.linspace(3000,6000,100)\n", + "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", + "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(x, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating and solving the model\n", + "\n", + "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eea07489478640aab13bd2aab1fe5020", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = pybamm.Simulation(spm, parameter_values=param)\n", + "t_eval = np.arange(0, 3600, 1)\n", + "sim.solve(t_eval=t_eval)\n", + "sim.plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[3] Charles R. Harris, K. Jarrod Millman, StΓ©fan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb index 905850a7fd..258c37c885 100644 --- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb @@ -23,6 +23,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -114,8 +115,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Safe: 153.951 ms\n", - "Fast: 88.029 ms\n" + "Safe: 125.714 ms\n", + "Fast: 77.698 ms\n" ] }, { @@ -160,17 +161,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 506.167 and h = 8.15178e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 506.167, , mxstep steps taken before reaching tout.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Safe: 403.072 ms\n", + "Safe: 7.791 s\n", "Solving fast mode, error occured: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n", "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n", - ".../casadi/interfaces/sundials/idas_interface.cpp:564: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n" + ".../casadi/interfaces/sundials/idas_interface.cpp:604: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n" ] }, { @@ -203,7 +204,7 @@ "try:\n", " sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n", "except pybamm.SolverError as e:\n", - " print(\"Solving fast mode, error occured:\", e.args[0])" + " print(\"Solving fast mode, error occurred:\", e.args[0])" ] }, { @@ -221,7 +222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99244556077647bcaf7b21b9b1c40acb", + "model_id": "d84d30bf7d8d4df1a330e8c9a69267a1", "version_major": 2, "version_minor": 0 }, @@ -345,12 +346,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "With dt_max=10, took 669.420 ms (integration time: 590.350 ms)\n", - "With dt_max=20, took 686.120 ms (integration time: 599.137 ms)\n", - "With dt_max=100, took 369.348 ms (integration time: 319.797 ms)\n", - "With dt_max=1000, took 91.474 ms (integration time: 57.455 ms)\n", - "With dt_max=3700, took 57.838 ms (integration time: 37.095 ms)\n", - "With 'fast' mode, took 49.520 ms (integration time: 37.400 ms)\n" + "With dt_max=10, took 610.021 ms (integration time: 534.636 ms)\n", + "With dt_max=20, took 686.939 ms (integration time: 536.861 ms)\n", + "With dt_max=100, took 338.657 ms (integration time: 291.815 ms)\n", + "With dt_max=1000, took 83.867 ms (integration time: 51.518 ms)\n", + "With dt_max=3700, took 52.384 ms (integration time: 32.960 ms)\n", + "With 'fast' mode, took 44.846 ms (integration time: 32.949 ms)\n" ] } ], @@ -395,20 +396,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "With dt_max=10, took 588.138 ms (integration time: 489.722 ms)\n", - "With dt_max=20, took 581.809 ms (integration time: 484.621 ms)\n", - "With dt_max=100, took 329.091 ms (integration time: 272.181 ms)\n", - "With dt_max=1000, took 113.543 ms (integration time: 69.477 ms)\n", - "With dt_max=3600, took 939.024 ms (integration time: 36.933 ms)\n" + "With dt_max=10, took 541.980 ms (integration time: 447.827 ms)\n", + "With dt_max=20, took 518.415 ms (integration time: 428.332 ms)\n", + "With dt_max=100, took 300.344 ms (integration time: 245.695 ms)\n", + "With dt_max=1000, took 101.787 ms (integration time: 60.608 ms)\n", + "With dt_max=3600, took 516.396 ms (integration time: 32.718 ms)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "At t = 460.712 and h = 1.83699e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 460.712, , mxstep steps taken before reaching tout.\n", - "At t = 460.712, , mxstep steps taken before reaching tout.\n" + "At t = 460.712 and h = 4.16966e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 460.712 and h = 5.11965e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 460.712 and h = 8.91111e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] } ], @@ -461,7 +462,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 828.886 ms\n" + "Took 813.671 ms\n" ] } ], @@ -524,7 +525,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 709.638 ms\n" + "Took 629.273 ms\n" ] }, { @@ -571,14 +572,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 1262.29 and h = 1.11482e-19, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 1262.29 and h = 1.0534e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Took 699.760 ms\n" + "Took 539.358 ms\n" ] }, { @@ -625,7 +626,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 309.979 ms\n" + "Took 289.618 ms\n" ] }, { @@ -809,7 +810,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -854,16 +855,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exact: 171.144 us\n", - "Smooth, k=5: 161.840 us\n", - "Smooth, k=10: 137.627 us\n", - "Smooth, k=100: 178.807 us\n" + "Exact: 172.240 us\n", + "Smooth, k=5: 161.790 us\n", + "Smooth, k=10: 150.367 us\n", + "Smooth, k=100: 193.054 us\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7ef8a815d03434bb3f1f4283d50018b", + "model_id": "13ea3acf77af43019375fb4f53395b28", "version_major": 2, "version_minor": 0 }, @@ -986,7 +987,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -1000,7 +1001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.16" }, "toc": { "base_numbering": 1, @@ -1017,7 +1018,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst index 787778fa01..ce5d7e0ca3 100644 --- a/docs/source/user_guide/installation/install-from-source.rst +++ b/docs/source/user_guide/installation/install-from-source.rst @@ -42,17 +42,17 @@ You can install the above with Where ``X`` is the version sub-number. + .. note:: + + On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or + follow the instructions on the `graphviz website `_. + .. tab:: MacOS .. code:: bash brew install python openblas gcc gfortran graphviz libomp -.. note:: - - On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or - follow the instructions on the `graphviz website `_. - Finally, we recommend using `Nox `_. You can install it with @@ -114,7 +114,7 @@ Using Nox (recommended) nox -s dev .. note:: - It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run. + It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run. This creates a virtual environment ``.nox/dev`` inside the ``PyBaMM/`` directory. It comes ready with PyBaMM and some useful development tools like `pre-commit `_ and `ruff `_. @@ -131,7 +131,7 @@ You can now activate the environment with .. code:: bash - .nox\dev\Scripts\activate.bat + .nox\dev\Scripts\activate.bat and run the tests to check your installation. diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst index 20e4129709..6ff48293bd 100644 --- a/docs/source/user_guide/installation/windows.rst +++ b/docs/source/user_guide/installation/windows.rst @@ -82,4 +82,4 @@ Installation using WSL If you want to install the optional PyBaMM solvers, you have to use the Windows Subsystem for Linux (WSL). You can find the installation -instructions `here `__. +instructions `here `__. diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py new file mode 100644 index 0000000000..4ceb4fc9f4 --- /dev/null +++ b/examples/scripts/MSMR.py @@ -0,0 +1,68 @@ +import pybamm + +pybamm.set_logging_level("INFO") + +# Use the MSMR model, with 6 negative electrode reactions and 4 positive electrode +# reactions +msmr_model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) + +# We can also use a SPM with MSMR thermodynamics, transport and kinetics by changing +# model options. Note we need to se the "surface form" to "algebraic" or "differential" +# to use the MSMR, since we cannot explicitly invert the kinetics +spm_msmr_model = pybamm.lithium_ion.SPM( + { + "number of MSMR reactions": ("6", "4"), + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + "surface form": "algebraic", + }, + name="Single Particle MSMR", +) + +# Load in the example MSMR parameter set +parameter_values = pybamm.ParameterValues("MSMR_Example") + +# Define an experiment +experiment = pybamm.Experiment( + [ + ( + "Discharge at 1C for 1 hour or until 3 V", + "Rest for 1 hour", + "Charge at C/3 until 4.2 V", + "Hold at 4.2 V until 10 mA", + "Rest for 1 hour", + ), + ] +) + +# Loop over the models, creating and solving a simulation +sols = [] +for model in [msmr_model, spm_msmr_model]: + sim = pybamm.Simulation( + model, parameter_values=parameter_values, experiment=experiment + ) + sol = sim.solve(initial_soc=0.9) + sols.append(sol) + +# Plot the fractional occupancy x_j of the individual MSMR reactions, along with some +# other variables of interest +xns = [ + f"Average x_n_{i}" for i in range(6) +] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5 +xps = [ + f"Average x_p_{i}" for i in range(4) +] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3 +pybamm.dynamic_plot( + sols, + [ + xns, + xps, + "Current [A]", + "Negative electrode interfacial current density [A.m-2]", + "Positive electrode interfacial current density [A.m-2]", + "Negative particle surface concentration [mol.m-3]", + "Positive particle surface concentration [mol.m-3]", + "Voltage [V]", + ], +) diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index 02a3199b80..b5cc23d946 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -53,6 +53,7 @@ plt.grid(True) plt.xlabel(r"Discharge Capacity (Ah)", fontsize=20) plt.ylabel(r"$\vert V - V_{comsol} \vert$", fontsize=20) +colors = iter(plt.cycler(color='bgrcmyk')) for key, C_rate in C_rates.items(): current = 24 * C_rate @@ -85,7 +86,7 @@ voltage_difference = np.abs(voltage_sol[0:end_index] - comsol_voltage[0:end_index]) # plot discharge curves and absolute voltage_difference - color = next(ax._get_lines.prop_cycler)["color"] + color = next(colors)["color"] discharge_curve.plot( comsol_discharge_capacity, comsol_voltage, color=color, linestyle=":" ) diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index 221d643683..21740584b5 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -36,6 +36,17 @@ @article{Andersson2019 doi = {10.1007/s12532-018-0139-4}, } +@article{Baker2018, + title={Multi-species, multi-reaction model for porous intercalation electrodes: Part I. Model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode}, + author={Baker, Daniel R and Verbrugge, Mark W}, + journal={Journal of The Electrochemical Society}, + volume={165}, + number={16}, + pages={A3952}, + year={2018}, + publisher={IOP Publishing} +} + @article{BrosaPlanella2021, title = {Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods}, author = {Brosa Planella, Ferran and Sheikh, Muhammad and Widanage, W. Dhammika}, @@ -502,6 +513,17 @@ @article{Valoen2005 publisher={IOP Publishing} } +@article{Verbrugge2017, + title={Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide}, + author={Verbrugge, Mark and Baker, Daniel and Koch, Brian and Xiao, Xingcheng and Gu, Wentian}, + journal={Journal of The Electrochemical Society}, + volume={164}, + number={11}, + pages={E3243}, + year={2017}, + publisher={IOP Publishing} +} + @article{Virtanen2020, title = {{SciPy 1.0: fundamental algorithms for scientific computing in Python}}, author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others}, diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py index edcaeb8f58..d1c45015b6 100644 --- a/pybamm/experiment/experiment.py +++ b/pybamm/experiment/experiment.py @@ -68,11 +68,6 @@ def __init__( termination, ) - self.datetime_formats = [ - "Day %j %H:%M:%S", - "%Y-%m-%d %H:%M:%S", - ] - operating_conditions_cycles = [] for cycle in operating_conditions: # Check types and convert to list @@ -89,21 +84,36 @@ def __init__( # Convert strings to pybamm.step._Step objects # We only do this once per unique step, do avoid unnecessary conversions - unique_steps_unprocessed = set(operating_conditions_steps_unprocessed) + # Assign experiment period and temperature if not specified in step + self.period = _convert_time_to_seconds(period) + self.temperature = _convert_temperature_to_kelvin(temperature) + processed_steps = {} - for step in unique_steps_unprocessed: - if isinstance(step, str): - processed_steps[step] = pybamm.step.string(step) + for step in operating_conditions_steps_unprocessed: + if repr(step) in processed_steps: + continue + elif isinstance(step, str): + processed_step = pybamm.step.string(step) elif isinstance(step, pybamm.step._Step): - processed_steps[step] = step + processed_step = step + + if processed_step.period is None: + processed_step.period = self.period + if processed_step.temperature is None: + processed_step.temperature = self.temperature + + processed_steps[repr(step)] = processed_step + + self.operating_conditions_steps = [ + processed_steps[repr(step)] + for step in operating_conditions_steps_unprocessed + ] # Save the processed unique steps and the processed operating conditions # for every step self.unique_steps = set(processed_steps.values()) - self.operating_conditions_steps = [ - processed_steps[step] for step in operating_conditions_steps_unprocessed - ] + # Allocate experiment global variables self.initial_start_time = self.operating_conditions_steps[0].start_time if ( @@ -118,15 +128,6 @@ def __init__( self.termination_string = termination self.termination = self.read_termination(termination) - # Modify steps with period and temperature in place - self.period = _convert_time_to_seconds(period) - self.temperature = _convert_temperature_to_kelvin(temperature) - for step in self.unique_steps: - if step.period is None: - step.period = self.period - if step.temperature is None: - step.temperature = self.temperature - def __str__(self): return str(self.operating_conditions_cycles) diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py index 7fb34a57b8..32cf2c002b 100644 --- a/pybamm/expression_tree/broadcasts.py +++ b/pybamm/expression_tree/broadcasts.py @@ -45,6 +45,11 @@ def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" return child + def _diff(self, variable): + """See :meth:`pybamm.Symbol._diff()`.""" + # Differentiate the child and broadcast the result in the same way + return self._unary_new_copy(self.child.diff(variable)) + class PrimaryBroadcast(Broadcast): """ diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py index dc80961a77..ae17a333ec 100644 --- a/pybamm/expression_tree/operations/evaluate_python.py +++ b/pybamm/expression_tree/operations/evaluate_python.py @@ -171,7 +171,6 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): if output_jax and scipy.sparse.issparse(value): # convert any remaining sparse matrices to our custom coo matrix constant_symbols[symbol.id] = create_jax_coo_matrix(value) - else: constant_symbols[symbol.id] = value return diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index d8550bb8ae..7f9c45775c 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -339,11 +339,6 @@ class with a :class:`Matrix` def __init__(self, name, child, domains=None): super().__init__(name, child, domains) - def diff(self, variable): - """See :meth:`pybamm.Symbol.diff()`.""" - # We shouldn't need this - raise NotImplementedError - class Gradient(SpatialOperator): """ diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py new file mode 100644 index 0000000000..fce5c7f068 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -0,0 +1,201 @@ +def electrolyte_diffusivity_Nyman2008(c_e, T): + """ + Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1] + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 + + # Nyman et al. (2008) does not provide temperature dependence + + return D_c_e + + +def electrolyte_conductivity_Nyman2008(c_e, T): + """ + Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1]. + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + sigma_e = ( + 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) + ) + + # Nyman et al. (2008) does not provide temperature dependence + + return sigma_e + + +def get_parameter_values(): + """ + Example parameter values for use with MSMR models. The thermodynamic parameters + are for Graphite and NMC622, and are taken from Table 1 of the paper + + Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao and Wentian Gu. + Thermodynamic Model for Substitutional Materials: Application to Lithiated + Graphite, Spinel Manganese Oxide, Iron Phosphate, and Layered + Nickel-Manganese-Cobalt Oxide. Journal of The Electrochemical Society, + 164(11):3243-3253, 2017. doi:10.1149/2.0341708jes. + + The remaining value are based on a parameterization of the LG M50 cell, from the + paper + + Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The + Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050. + + and references therein. Verbrugge et al. (2017) does not provide kinetic parameters + so we set the reference exchange current density to 5 A.m-2 for the positive + electrode reactions and 2.7 A.m-2 for the negative electrode reactions, which are + the values used in the Chen et al. (2020) paper. We also assume that the + exchange-current density is symmetric. Note: the 4th reaction in the positive + electrode gave unphysical results so we set the reference exchange current density + and symmetry factor to 1e6 and 1, respectively. The parameter values are intended + to serve as an example set to use with the MSMR model and do not claim to match any + experimental cycling data. + """ + return { + "chemistry": "lithium_ion", + # cell + "Negative electrode thickness [m]": 8.52e-05, + "Separator thickness [m]": 1.2e-05, + "Positive electrode thickness [m]": 7.56e-05, + "Electrode height [m]": 0.065, + "Electrode width [m]": 1.58, + "Nominal cell capacity [A.h]": 5.0, + "Current function [A]": 5.0, + "Contact resistance [Ohm]": 0, + # negative electrode + "Number of reactions in negative electrode": 6, + "U0_n_0": 0.08843, + "X_n_0": 0.43336, + "w_n_0": 0.08611, + "a_n_0": 0.5, + "j0_ref_n_0": 2.7, + "U0_n_1": 0.12799, + "X_n_1": 0.23963, + "w_n_1": 0.08009, + "a_n_1": 0.5, + "j0_ref_n_1": 2.7, + "U0_n_2": 0.14331, + "X_n_2": 0.15018, + "w_n_2": 0.72469, + "a_n_2": 0.5, + "j0_ref_n_2": 2.7, + "U0_n_3": 0.16984, + "X_n_3": 0.05462, + "w_n_3": 2.53277, + "a_n_3": 0.5, + "j0_ref_n_3": 2.7, + "U0_n_4": 0.21446, + "X_n_4": 0.06744, + "w_n_4": 0.09470, + "a_n_4": 0.5, + "j0_ref_n_4": 2.7, + "U0_n_5": 0.36325, + "X_n_5": 0.05476, + "w_n_5": 5.97354, + "a_n_5": 0.5, + "j0_ref_n_5": 2.7, + "Negative electrode conductivity [S.m-1]": 215.0, + "Maximum concentration in negative electrode [mol.m-3]": 33133.0, + "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative electrode porosity": 0.25, + "Negative electrode active material volume fraction": 0.75, + "Negative particle radius [m]": 5.86e-06, + "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, + "Negative electrode Bruggeman coefficient (electrode)": 0, + "Negative electrode OCP entropic change [V.K-1]": 0.0, + # positive electrode + "Number of reactions in positive electrode": 4, + "U0_p_0": 3.62274, + "X_p_0": 0.13442, + "w_p_0": 0.96710, + "a_p_0": 0.5, + "j0_ref_p_0": 5, + "U0_p_1": 3.72645, + "X_p_1": 0.32460, + "w_p_1": 1.39712, + "a_p_1": 0.5, + "j0_ref_p_1": 5, + "U0_p_2": 3.90575, + "X_p_2": 0.21118, + "w_p_2": 3.50500, + "a_p_2": 0.5, + "j0_ref_p_2": 5, + "U0_p_3": 4.22955, + "X_p_3": 0.32980, + "w_p_3": 5.52757, + "a_p_3": 1, + "j0_ref_p_3": 1e6, + "Positive electrode conductivity [S.m-1]": 0.18, + "Maximum concentration in positive electrode [mol.m-3]": 63104.0, + "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive electrode porosity": 0.335, + "Positive electrode active material volume fraction": 0.665, + "Positive particle radius [m]": 5.22e-06, + "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, + "Positive electrode Bruggeman coefficient (electrode)": 0, + "Positive electrode OCP entropic change [V.K-1]": 0.0, + # separator + "Separator porosity": 0.47, + "Separator Bruggeman coefficient (electrolyte)": 1.5, + # electrolyte + "Initial concentration in electrolyte [mol.m-3]": 1000.0, + "Cation transference number": 0.2594, + "Thermodynamic factor": 1.0, + "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008, + "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008, + # experiment + "Reference temperature [K]": 298.15, + "Total heat transfer coefficient [W.m-2.K-1]": 10.0, + "Ambient temperature [K]": 298.15, + "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of cells connected in series to make a battery": 1.0, + "Lower voltage cut-off [V]": 2.8, + "Upper voltage cut-off [V]": 4.2, + "Open-circuit voltage at 0% SOC [V]": 2.8, + "Open-circuit voltage at 100% SOC [V]": 4.2, + "Initial temperature [K]": 298.15, + "Initial voltage in negative electrode [V]": 0.01, + "Initial voltage in positive electrode [V]": 4.19, + # citations + "citations": ["Verbrugge2017", "Baker2018", "Chen2020"], + } diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 64ce55ebda..ad36786381 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -7,6 +7,16 @@ import warnings +def represents_positive_integer(s): + """Check if a string represents a positive integer""" + try: + val = int(s) + except ValueError: + return False + else: + return val > 0 + + class BatteryModelOptions(pybamm.FuzzyDict): """ Attributes @@ -64,10 +74,10 @@ class BatteryModelOptions(pybamm.FuzzyDict): "surface form" cannot be 'false'. * "intercalation kinetics" : str Model for intercalation kinetics. Can be "symmetric Butler-Volmer" - (default), "asymmetric Butler-Volmer", "linear", "Marcus", or - "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014). - A 2-tuple can be provided for different behaviour in negative and - positive electrodes. + (default), "asymmetric Butler-Volmer", "linear", "Marcus", + "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014), + or "MSMR" (which uses the form from Baker 2018). A 2-tuple can be + provided for different behaviour in negative and positive electrodes. * "interface utilisation": str Can be "full" (default), "constant", or "current-driven". * "lithium plating" : str @@ -82,9 +92,17 @@ class BatteryModelOptions(pybamm.FuzzyDict): "stress and reaction-driven". A 2-tuple can be provided for different behaviour in negative and positive electrodes. + * "number of MSMR reactions" : str + Sets the number of reactions to use in the MSMR model in each electrode. + A 2-tuple can be provided to give a different number of reactions in + the negative and positive electrodes. Default is "none". Can be any + 2-tuple of strings of integers. For example, set to ("6", "4") for a + negative electrode with 6 reactions and a positive electrode with 4 + reactions. * "open-circuit potential" : str Sets the model for the open circuit potential. Can be "single" - (default) or "current sigmoid". A 2-tuple can be provided for different + (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle" + option must also be "MSMR". A 2-tuple can be provided for different behaviour in negative and positive electrodes. * "operating mode" : str Sets the operating mode for the model. This determines how the current @@ -105,8 +123,9 @@ class BatteryModelOptions(pybamm.FuzzyDict): * "particle" : str Sets the submodel to use to describe behaviour within the particle. Can be "Fickian diffusion" (default), "uniform profile", - "quadratic profile", or "quartic profile". A 2-tuple can be provided - for different behaviour in negative and positive electrodes. + "quadratic profile", "quartic profile", or "MSMR". If "MSMR" then the + "open-circuit potential" option must also be "MSMR". A 2-tuple can be + provided for different behaviour in negative and positive electrodes. * "particle mechanics" : str Sets the model to account for mechanical effects such as particle swelling and cracking. Can be "none" (default), "swelling only", @@ -225,6 +244,7 @@ def __init__(self, extra_options): "linear", "Marcus", "Marcus-Hush-Chidsey", + "MSMR", ], "interface utilisation": ["full", "constant", "current-driven"], "lithium plating": [ @@ -241,7 +261,8 @@ def __init__(self, extra_options): "current-driven", "stress and reaction-driven", ], - "open-circuit potential": ["single", "current sigmoid"], + "number of MSMR reactions": ["none"], + "open-circuit potential": ["single", "current sigmoid", "MSMR"], "operating mode": [ "current", "voltage", @@ -259,6 +280,7 @@ def __init__(self, extra_options): "uniform profile", "quadratic profile", "quartic profile", + "MSMR", ], "particle mechanics": ["none", "swelling only", "swelling and cracking"], "particle phases": ["1", "2"], @@ -396,6 +418,25 @@ def __init__(self, extra_options): ) ) + # If any of "open-circuit potential", "particle" or "intercalation kinetics" is + # "MSMR" then all of them must be "MSMR". + # Note: this check is currently performed on full cells, but is loosened for + # half-cells where you must pass a tuple of options to only set MSMR models in + # the working electrode + msmr_check_list = [ + options[opt] == "MSMR" + for opt in ["open-circuit potential", "particle", "intercalation kinetics"] + ] + if ( + options["working electrode"] == "both" + and any(msmr_check_list) + and not all(msmr_check_list) + ): + raise pybamm.OptionError( + "If any of 'open-circuit potential', 'particle' or " + "'intercalation kinetics' is 'MSMR' then all of them must be 'MSMR'" + ) + # If "SEI film resistance" is "distributed" then "total interfacial current # density as a state" must be "true" if options["SEI film resistance"] == "distributed": @@ -575,6 +616,7 @@ def __init__(self, extra_options): "intercalation kinetics", "interface utilisation", "loss of active material", + "number of MSMR reactions", "open-circuit potential", "particle", "particle mechanics", @@ -602,7 +644,16 @@ def __init__(self, extra_options): value_list.append(val) for val in value_list: if val not in self.possible_options[option]: - if not (option == "operating mode" and callable(val)): + if option == "operating mode" and callable(val): + # "operating mode" can be a function + pass + elif ( + option == "number of MSMR reactions" + and represents_positive_integer(val) + ): + # "number of MSMR reactions" can be a positive integer + pass + else: raise pybamm.OptionError( f"\n'{val}' is not recognized in option '{option}'. " f"Possible values are {self.possible_options[option]}" @@ -879,6 +930,10 @@ def options(self, extra_options): raise pybamm.OptionError("Lead-acid models cannot have SEI formation") if options["lithium plating"] != "none": raise pybamm.OptionError("Lead-acid models cannot have lithium plating") + if options["open-circuit potential"] == "MSMR": + raise pybamm.OptionError( + "Lead-acid models cannot use the MSMR open-circuit potential model" + ) if ( isinstance(self, pybamm.lead_acid.LOQS) @@ -1003,6 +1058,8 @@ def get_intercalation_kinetics(self, domain): return pybamm.kinetics.Marcus elif options["intercalation kinetics"] == "Marcus-Hush-Chidsey": return pybamm.kinetics.MarcusHushChidsey + elif options["intercalation kinetics"] == "MSMR": + return pybamm.kinetics.MSMRButlerVolmer def get_inverse_intercalation_kinetics(self): if self.options["intercalation kinetics"] == "symmetric Butler-Volmer": @@ -1241,17 +1298,10 @@ def set_voltage_variables(self): "Battery voltage [V]": V * num_cells, } ) - # Variables for calculating the equivalent circuit model (ECM) resistance - # Need to compare OCV to initial value to capture this as an overpotential - ocv_init = self.param.ocv_init - eta_ocv = ocv_bulk - ocv_init - # Current collector current density for working out euiqvalent resistance - # based on Ohm's Law - i_cc = self.variables["Current collector current density [A.m-2]"] + + # Calculate equivalent resistance of an OCV-R Equivalent Circuit Model # ECM overvoltage is OCV minus voltage v_ecm = ocv_bulk - V - # Current collector area for turning resistivity into resistance - A_cc = self.param.A_cc # Hack to avoid division by zero if i_cc is exactly zero # If i_cc is zero, i_cc_not_zero becomes 1. But multiplying by sign(i_cc) makes @@ -1259,11 +1309,12 @@ def set_voltage_variables(self): def x_not_zero(x): return ((x > 0) + (x < 0)) * x + (x >= 0) * (x <= 0) + i_cc = self.variables["Current collector current density [A.m-2]"] i_cc_not_zero = x_not_zero(i_cc) + A_cc = self.param.A_cc self.variables.update( { - "Change in open-circuit voltage [V]": eta_ocv, "Local ECM resistance [Ohm]": pybamm.sign(i_cc) * v_ecm / (i_cc_not_zero * A_cc), diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 76625858e3..95a5059f5a 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -6,6 +6,8 @@ ElectrodeSOHSolver, get_initial_stoichiometries, get_min_max_stoichiometries, + get_initial_ocps, + get_min_max_ocps, ) from .electrode_soh_half_cell import ElectrodeSOHHalfCell from .spm import SPM @@ -18,3 +20,4 @@ from .basic_dfn_composite import BasicDFNComposite from .Yang2017 import Yang2017 from .mpm import MPM +from .msmr import MSMR diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index cc615dacf7..41e4670cf7 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -238,6 +238,8 @@ def set_open_circuit_potential_submodel(self): ocp_model = ocp_submodels.SingleOpenCircuitPotential elif ocp_option == "current sigmoid": ocp_model = ocp_submodels.CurrentSigmoidOpenCircuitPotential + elif ocp_option == "MSMR": + ocp_model = ocp_submodels.MSMROpenCircuitPotential self.submodels[f"{domain} {phase} open-circuit potential"] = ocp_model( self.param, domain, reaction, self.options, phase ) diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py index 6fcebc636d..5f0a2cfb3e 100644 --- a/pybamm/models/full_battery_models/lithium_ion/dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py @@ -66,6 +66,10 @@ def set_particle_submodel(self): submod = pybamm.particle.PolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=False + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 92996675d4..c6a445f316 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -7,11 +7,80 @@ import warnings -class _ElectrodeSOH(pybamm.BaseModel): - """Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`. - This model is mainly for internal use, to calculate summary variables in a - simulation. - Some of the output variables are defined in [2]_. +class _BaseElectrodeSOH(pybamm.BaseModel): + def __init__(self): + pybamm.citations.register("Mohtat2019") + pybamm.citations.register("Weng2023") + name = "ElectrodeSOH model" + super().__init__(name) + + def get_100_soc_variables( + self, x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ): + Acc_cm2 = param.A_cc * 1e4 + variables = { + "x_100": x_100, + "y_100": y_100, + "Un(x_100)": Un_100, + "Up(y_100)": Up_100, + "Up(y_100) - Un(x_100)": Up_100 - Un_100, + "Q_Li": Q_Li, + "n_Li": Q_Li * 3600 / param.F, + "Q_n": Q_n, + "Q_p": Q_p, + "Cyclable lithium capacity [A.h]": Q_Li, + "Negative electrode capacity [A.h]": Q_n, + "Positive electrode capacity [A.h]": Q_p, + "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, + "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, + "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, + # eq 33 of Weng2023 + "Formation capacity loss [A.h]": Q_p - Q_Li, + "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, + # eq 26 of Weng2024 + "Negative positive ratio": Q_n / Q_p, + "NPR": Q_n / Q_p, + } + return variables + + def get_0_soc_variables( + self, x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ): + Acc_cm2 = param.A_cc * 1e4 + # eq 27 of Weng2023 + Q_n_excess = Q_n * (1 - x_100) + NPR_practical = 1 + Q_n_excess / Q + variables = { + "Q": Q, + "Capacity [A.h]": Q, + "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, + "x_0": x_0, + "y_0": y_0, + "Un(x_0)": Un_0, + "Up(y_0)": Up_0, + "Up(y_0) - Un(x_0)": Up_0 - Un_0, + "x_100 - x_0": x_100 - x_0, + "y_0 - y_100": y_0 - y_100, + "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), + "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), + "Negative electrode excess capacity ratio": Q_n / Q, + "Positive electrode excess capacity ratio": Q_p / Q, + "Practical negative positive ratio": NPR_practical, + "Practical NPR": NPR_practical, + } + return variables + + @property + def default_solver(self): + # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings + return pybamm.AlgebraicSolver() + + +class _ElectrodeSOH(_BaseElectrodeSOH): + """ + Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`. This + model is mainly for internal use, to calculate summary variables in a simulation. + Some of the output variables are defined in :footcite:t:`Weng2023`. .. math:: Q_{Li} = y_{100}Q_p + x_{100}Q_n, @@ -29,10 +98,7 @@ class _ElectrodeSOH(pybamm.BaseModel): def __init__( self, param=None, solve_for=None, known_value="cyclable lithium capacity" ): - pybamm.citations.register("Mohtat2019") - pybamm.citations.register("Weng2023") - name = "ElectrodeSOH model" - super().__init__(name) + super().__init__() param = param or pybamm.LithiumIonParameters() solve_for = solve_for or ["x_0", "x_100"] @@ -47,8 +113,8 @@ def __init__( Up = param.p.prim.U T_ref = param.T_ref - V_max = param.opc_soc_100_dimensional - V_min = param.opc_soc_0_dimensional + V_max = param.ocp_soc_100_dimensional + V_min = param.ocp_soc_0_dimensional Q_n = pybamm.InputParameter("Q_n") Q_p = pybamm.InputParameter("Q_p") @@ -77,30 +143,9 @@ def __init__( self.initial_conditions[x_100] = pybamm.Scalar(0.9) # These variables are defined in all cases - Acc_cm2 = param.A_cc * 1e4 - self.variables = { - "x_100": x_100, - "y_100": y_100, - "Un(x_100)": Un_100, - "Up(y_100)": Up_100, - "Up(y_100) - Un(x_100)": Up_100 - Un_100, - "Q_Li": Q_Li, - "n_Li": Q_Li * 3600 / param.F, - "Q_n": Q_n, - "Q_p": Q_p, - "Cyclable lithium capacity [A.h]": Q_Li, - "Negative electrode capacity [A.h]": Q_n, - "Positive electrode capacity [A.h]": Q_p, - "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, - "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, - "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, - # eq 33 of Weng2023 - "Formation capacity loss [A.h]": Q_p - Q_Li, - "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, - # eq 26 of Weng2024 - "Negative positive ratio": Q_n / Q_p, - "NPR": Q_n / Q_p, - } + self.variables = self.get_100_soc_variables( + x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ) # Define variables and equations for 0% state of charge if "x_0" in solve_for: @@ -112,7 +157,6 @@ def __init__( var = x_0 elif known_value == "cell capacity": x_0 = x_100 - Q / Q_n - Q_Li = y_100 * Q_p + x_0 * Q_n # the variable we are solving for is y_100, since x_0 is calculated # based on Q var = y_100 @@ -123,34 +167,97 @@ def __init__( self.initial_conditions[var] = pybamm.Scalar(0.1) # These variables are only defined if x_0 is solved for - # eq 27 of Weng2023 - Q_n_excess = Q_n * (1 - x_100) - NPR_practical = 1 + Q_n_excess / Q self.variables.update( - { - "Q": Q, - "Capacity [A.h]": Q, - "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, - "x_0": x_0, - "y_0": y_0, - "Un(x_0)": Un_0, - "Up(y_0)": Up_0, - "Up(y_0) - Un(x_0)": Up_0 - Un_0, - "x_100 - x_0": x_100 - x_0, - "y_0 - y_100": y_0 - y_100, - "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), - "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), - "Negative electrode excess capacity ratio": Q_n / Q, - "Positive electrode excess capacity ratio": Q_p / Q, - "Practical negative positive ratio": NPR_practical, - "Practical NPR": NPR_practical, - } + self.get_0_soc_variables( + x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ) ) - @property - def default_solver(self): - # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings - return pybamm.AlgebraicSolver() + +class _ElectrodeSOHMSMR(_BaseElectrodeSOH): + """ + Model to calculate electrode-specific SOH using the MSMR formulation from + :footcite:t:`Baker2018`. See :class:`_ElectrodeSOH` for more details. + """ + + def __init__( + self, param=None, solve_for=None, known_value="cyclable lithium capacity" + ): + pybamm.citations.register("Baker2018") + super().__init__() + + param = param or pybamm.LithiumIonParameters({"open-circuit potential": "MSMR"}) + solve_for = solve_for or ["Un_0", "Un_100"] + + if known_value == "cell capacity" and solve_for != ["Un_0", "Un_100"]: + raise ValueError( + "If known_value is 'cell capacity', solve_for must be " + "['Un_0', 'Un_100']" + ) + + # Define parameters and input parameters + x_n = param.n.prim.x + x_p = param.p.prim.x + + V_max = param.voltage_high_cut + V_min = param.voltage_low_cut + Q_n = pybamm.InputParameter("Q_n") + Q_p = pybamm.InputParameter("Q_p") + + if known_value == "cyclable lithium capacity": + Q_Li = pybamm.InputParameter("Q_Li") + elif known_value == "cell capacity": + Q = pybamm.InputParameter("Q") + + # Define variables for 0% state of charge + # TODO: thermal effects (include dU/dT) + if "Un_0" in solve_for: + Un_0 = pybamm.Variable("Un(x_0)") + Up_0 = V_min + Un_0 + x_0 = x_n(Un_0) + y_0 = x_p(Up_0) + + # Define variables for 100% state of charge + # TODO: thermal effects (include dU/dT) + if "Un_100" in solve_for: + Un_100 = pybamm.Variable("Un(x_100)") + Up_100 = V_max + Un_100 + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) + else: + Un_100 = pybamm.InputParameter("Un(x_100)") + Up_100 = pybamm.InputParameter("Up(y_100)") + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) + + # Define equations for 100% state of charge + if "Un_100" in solve_for: + if known_value == "cyclable lithium capacity": + Un_100_eqn = Q_Li - y_100 * Q_p - x_100 * Q_n + elif known_value == "cell capacity": + Un_100_eqn = x_100 - x_0 - Q / Q_n + Q_Li = y_100 * Q_p + x_100 * Q_n + self.algebraic[Un_100] = Un_100_eqn + self.initial_conditions[Un_100] = pybamm.Scalar(0) # better ic? + + # These variables are defined in all cases + self.variables = self.get_100_soc_variables( + x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ) + + # Define equation for 0% state of charge + if "Un_0" in solve_for: + if known_value == "cyclable lithium capacity": + Q = Q_n * (x_100 - x_0) + self.algebraic[Un_0] = y_100 - y_0 + Q / Q_p + self.initial_conditions[Un_0] = pybamm.Scalar(1) # better ic? + + # These variables are only defined if x_0 is solved for + self.variables.update( + self.get_0_soc_variables( + x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ) + ) class ElectrodeSOHSolver: @@ -167,19 +274,47 @@ class ElectrodeSOHSolver: known_value : str, optional The known value needed to complete the electrode SOH model. Can be "cyclable lithium capacity" (default) or "cell capacity". - + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. """ def __init__( - self, parameter_values, param=None, known_value="cyclable lithium capacity" + self, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, ): self.parameter_values = parameter_values - self.param = param or pybamm.LithiumIonParameters() + self.param = param or pybamm.LithiumIonParameters(options) self.known_value = known_value + self.options = options or pybamm.BatteryModelOptions({}) + + self.lims_ocp = self._get_lims_ocp() + self.OCV_function = None + self._get_electrode_soh_sims_full = lru_cache()( + self.__get_electrode_soh_sims_full + ) + self._get_electrode_soh_sims_split = lru_cache()( + self.__get_electrode_soh_sims_split + ) + + def _get_lims_ocp(self): + parameter_values = self.parameter_values # Check whether each electrode OCP is a function (False) or data (True) - OCPp_data = isinstance(parameter_values["Positive electrode OCP [V]"], tuple) - OCPn_data = isinstance(parameter_values["Negative electrode OCP [V]"], tuple) + # Set to false for MSMR models + if self.options["open-circuit potential"] == "MSMR": + OCPp_data = False + OCPn_data = False + else: + OCPp_data = isinstance( + parameter_values["Positive electrode OCP [V]"], tuple + ) + OCPn_data = isinstance( + parameter_values["Negative electrode OCP [V]"], tuple + ) # Calculate stoich limits for the open-circuit potentials if OCPp_data: @@ -197,28 +332,33 @@ def __init__( else: x0_min = 1e-6 x100_max = 1 - 1e-6 - - self.lims_ocp = (x0_min, x100_max, y100_min, y0_max) - self.OCV_function = None - self._get_electrode_soh_sims_full = lru_cache()( - self.__get_electrode_soh_sims_full - ) - self._get_electrode_soh_sims_split = lru_cache()( - self.__get_electrode_soh_sims_split - ) + return (x0_min, x100_max, y100_min, y0_max) def __get_electrode_soh_sims_full(self): - full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value) + if self.options["open-circuit potential"] == "MSMR": + full_model = _ElectrodeSOHMSMR( + param=self.param, known_value=self.known_value + ) + else: + full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value) return pybamm.Simulation(full_model, parameter_values=self.parameter_values) def __get_electrode_soh_sims_split(self): - x100_model = _ElectrodeSOH( - param=self.param, solve_for=["x_100"], known_value=self.known_value - ) + if self.options["open-circuit potential"] == "MSMR": + x100_model = _ElectrodeSOHMSMR( + param=self.param, solve_for=["Un_100"], known_value=self.known_value + ) + x0_model = _ElectrodeSOHMSMR( + param=self.param, solve_for=["Un_0"], known_value=self.known_value + ) + else: + x100_model = _ElectrodeSOH( + param=self.param, solve_for=["x_100"], known_value=self.known_value + ) + x0_model = _ElectrodeSOH( + param=self.param, solve_for=["x_0"], known_value=self.known_value + ) x100_sim = pybamm.Simulation(x100_model, parameter_values=self.parameter_values) - x0_model = _ElectrodeSOH( - param=self.param, solve_for=["x_0"], known_value=self.known_value - ) x0_sim = pybamm.Simulation(x0_model, parameter_values=self.parameter_values) return [x100_sim, x0_sim] @@ -268,30 +408,35 @@ def solve(self, inputs): sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()} # Calculate theoretical energy - energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - self.parameter_values, sol_dict - ) - sol_dict.update({"Maximum theoretical energy [W.h]": energy}) + # TODO: energy calc for MSMR + if self.options["open-circuit potential"] != "MSMR": + energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( + self.parameter_values, + sol_dict, + ) + sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict def _set_up_solve(self, inputs): # Try with full sim sim = self._get_electrode_soh_sims_full() if sim.solution is not None: - x100_sol = sim.solution["x_100"].data - x0_sol = sim.solution["x_0"].data - y100_sol = sim.solution["y_100"].data - y0_sol = sim.solution["y_0"].data - return {"x_100": x100_sol, "x_0": x0_sol, "y_100": y100_sol, "y_0": y0_sol} - - # Try with split sims - if self.known_value == "cyclable lithium capacity": - x100_sim, x0_sim = self._get_electrode_soh_sims_split() - if x100_sim.solution is not None and x0_sim.solution is not None: - x100_sol = x100_sim.solution["x_100"].data - x0_sol = x0_sim.solution["x_0"].data - y100_sol = x100_sim.solution["y_100"].data - y0_sol = x0_sim.solution["y_0"].data + if self.options["open-circuit potential"] == "MSMR": + Un_100_sol = sim.solution["Un(x_100)"].data + Un_0_sol = sim.solution["Un(x_0)"].data + Up_100_sol = sim.solution["Up(y_100)"].data + Up_0_sol = sim.solution["Up(y_0)"].data + return { + "Un(x_100)": Un_100_sol, + "Un(x_0)": Un_0_sol, + "Up(x_100)": Up_100_sol, + "Up(x_0)": Up_0_sol, + } + else: + x100_sol = sim.solution["x_100"].data + x0_sol = sim.solution["x_0"].data + y100_sol = sim.solution["y_100"].data + y0_sol = sim.solution["y_0"].data return { "x_100": x100_sol, "x_0": x0_sol, @@ -299,6 +444,33 @@ def _set_up_solve(self, inputs): "y_0": y0_sol, } + # Try with split sims + if self.known_value == "cyclable lithium capacity": + x100_sim, x0_sim = self._get_electrode_soh_sims_split() + if x100_sim.solution is not None and x0_sim.solution is not None: + if self.options["open-circuit potential"] == "MSMR": + Un_100_sol = x100_sim.solution["Un_100"].data + Un_0_sol = x0_sim.solution["Un_0"].data + Up_100_sol = x100_sim.solution["Up_100"].data + Up_0_sol = x0_sim.solution["Up_0"].data + return { + "Un(x_100)": Un_100_sol, + "Un(x_0)": Un_0_sol, + "Up(x_100)": Up_100_sol, + "Up(x_0)": Up_0_sol, + } + else: + x100_sol = x100_sim.solution["x_100"].data + x0_sol = x0_sim.solution["x_0"].data + y100_sol = x100_sim.solution["y_100"].data + y0_sol = x0_sim.solution["y_0"].data + return { + "x_100": x100_sol, + "x_0": x0_sol, + "y_100": y100_sol, + "y_0": y0_sol, + } + # Fall back to initial conditions calculated from limits x0_min, x100_max, y100_min, y0_max = self._get_lims(inputs) if self.known_value == "cyclable lithium capacity": @@ -321,7 +493,29 @@ def _set_up_solve(self, inputs): x0_init = np.maximum(x100_max - Q / Q_n, 0.1) y100_init = np.maximum(y0_max - Q / Q_p, 0.1) y0_init = np.minimum(y100_min + Q / Q_p, 0.9) - return {"x_100": x100_init, "x_0": x0_init, "y_100": y100_init, "y_0": y0_init} + if self.options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol0 = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x0_init, "y": y0_init} + ) + sol100 = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x100_init, "y": y100_init} + ) + return { + "Un(x_100)": sol100["Un"].data, + "Un(x_0)": sol0["Un"].data, + "Up(y_100)": sol100["Up"].data, + "Up(y_0)": sol0["Up"].data, + } + else: + return { + "x_100": x100_init, + "x_0": x0_init, + "y_100": y100_init, + "y_0": y0_init, + } def _solve_full(self, inputs, ics): sim = self._get_electrode_soh_sims_full() @@ -335,9 +529,12 @@ def _solve_split(self, inputs, ics): x100_sim.build() x100_sim.built_model.set_initial_conditions_from(ics) x100_sol = x100_sim.solve([0], inputs=inputs) - - inputs["x_100"] = x100_sol["x_100"].data[0] - inputs["y_100"] = x100_sol["y_100"].data[0] + if self.options["open-circuit potential"] == "MSMR": + inputs["Un(x_100)"] = x100_sol["Un(x_100)"].data[0] + inputs["Up(y_100)"] = x100_sol["Up(y_100)"].data[0] + else: + inputs["x_100"] = x100_sol["x_100"].data[0] + inputs["y_100"] = x100_sol["y_100"].data[0] x0_sim.build() x0_sim.built_model.set_initial_conditions_from(ics) x0_sol = x0_sim.solve([0], inputs=inputs) @@ -404,29 +601,53 @@ def _check_esoh_feasible(self, inputs): # Parameterize the OCP functions if self.OCV_function is None: - T = self.parameter_values["Reference temperature [K]"] - x = pybamm.InputParameter("x") - y = pybamm.InputParameter("y") self.V_max = self.parameter_values.evaluate( - self.param.opc_soc_100_dimensional + self.param.ocp_soc_100_dimensional ) self.V_min = self.parameter_values.evaluate( - self.param.opc_soc_0_dimensional + self.param.ocp_soc_0_dimensional + ) + if self.options["open-circuit potential"] == "MSMR": + # will solve for potentials at the sto limits, so no need + # to store a function + self.OCV_function = "MSMR" + else: + T = self.parameter_values["Reference temperature [K]"] + x = pybamm.InputParameter("x") + y = pybamm.InputParameter("y") + self.OCV_function = self.parameter_values.process_symbol( + self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T) + ) + + # Evaluate OCP function + if self.options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol0 = pybamm.AlgebraicSolver(tol=1e-4).solve( + msmr_pot_model, inputs={"x": x0_min, "y": y0_max} + ) + sol100 = pybamm.AlgebraicSolver(tol=1e-4).solve( + msmr_pot_model, inputs={"x": x100_max, "y": y100_min} + ) + Up0 = sol0["Up"].data[0] + Un0 = sol0["Un"].data[0] + Up100 = sol100["Up"].data[0] + Un100 = sol100["Un"].data[0] + V_lower_bound = float(Up0 - Un0) + V_upper_bound = float(Up100 - Un100) + else: + # address numpy 1.25 deprecation warning: array should have ndim=0 + # before conversion + V_lower_bound = float( + self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item() ) - self.OCV_function = self.parameter_values.process_symbol( - self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T) + V_upper_bound = float( + self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item() ) # Check that the min and max achievable voltages span wider than the desired # voltage range - # address numpy 1.25 deprecation warning: array should have ndim=0 - # before conversion - V_lower_bound = float( - self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item() - ) - V_upper_bound = float( - self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item() - ) if V_lower_bound > self.V_min: raise ( ValueError( @@ -471,8 +692,8 @@ def get_initial_stoichiometries(self, initial_value): if isinstance(initial_value, str) and initial_value.endswith("V"): V_init = float(initial_value[:-1]) - V_min = parameter_values.evaluate(param.opc_soc_0_dimensional) - V_max = parameter_values.evaluate(param.opc_soc_100_dimensional) + V_min = parameter_values.evaluate(param.ocp_soc_0_dimensional) + V_max = parameter_values.evaluate(param.ocp_soc_100_dimensional) if not V_min < V_init < V_max: raise ValueError( @@ -483,13 +704,23 @@ def get_initial_stoichiometries(self, initial_value): # Solve simple model for initial soc based on target voltage soc_model = pybamm.BaseModel() soc = pybamm.Variable("soc") - Up = param.p.prim.U - Un = param.n.prim.U - T_ref = parameter_values["Reference temperature [K]"] x = x_0 + soc * (x_100 - x_0) y = y_0 - soc * (y_0 - y_100) - - soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init + if self.options["open-circuit potential"] == "MSMR": + xn = param.n.prim.x + xp = param.p.prim.x + Up = pybamm.Variable("Up") + Un = pybamm.Variable("Un") + soc_model.algebraic[Up] = x - xn(Un) + soc_model.algebraic[Un] = y - xp(Up) + soc_model.initial_conditions[Un] = 0 + soc_model.initial_conditions[Up] = V_max + soc_model.algebraic[soc] = Up - Un - V_init + else: + Up = param.p.prim.U + Un = param.n.prim.U + T_ref = parameter_values["Reference temperature [K]"] + soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init # initial guess for soc linearly interpolates between 0 and 1 # based on V linearly interpolating between V_max and V_min soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min) @@ -538,9 +769,73 @@ def get_min_max_stoichiometries(self): sol = self.solve(inputs) return [sol["x_0"], sol["x_100"], sol["y_100"], sol["y_0"]] + def get_initial_ocps(self, initial_value): + """ + Calculate initial open-circuit potentials to start off the simulation at a + particular state of charge, given voltage limits, open-circuit potentials, etc + defined by parameter_values + + Parameters + ---------- + initial_value : float + Target SOC, must be between 0 and 1. + + Returns + ------- + Un, Up + The initial open-circuit potentials at the desired initial state of charge + """ + parameter_values = self.parameter_values + param = self.param + x, y = self.get_initial_stoichiometries(initial_value) + if self.options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x, "y": y} + ) + Un = sol["Un"].data[0] + Up = sol["Up"].data[0] + else: + T_ref = parameter_values["Reference temperature [K]"] + Un = parameter_values.evaluate(param.n.prim.U(x, T_ref)) + Up = parameter_values.evaluate(param.p.prim.U(y, T_ref)) + return Un, Up + + def get_min_max_ocps(self): + """ + Calculate min/max open-circuit potentials + given voltage limits, open-circuit potentials, etc defined by parameter_values + + Returns + ------- + Un_0, Un_100, Up_100, Up_0 + The min/max ocps + """ + parameter_values = self.parameter_values + param = self.param + + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + + if self.known_value == "cyclable lithium capacity": + Q_Li = parameter_values.evaluate(param.Q_Li_particles_init) + inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} + elif self.known_value == "cell capacity": + Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel) + inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q": Q} + # Solve the model and check outputs + sol = self.solve(inputs) + return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]] + def get_initial_stoichiometries( - initial_value, parameter_values, param=None, known_value="cyclable lithium capacity" + initial_value, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, ): """ Calculate initial stoichiometries to start off the simulation at a particular @@ -559,18 +854,24 @@ def get_initial_stoichiometries( param : :class:`pybamm.LithiumIonParameters`, optional The symbolic parameter set to use for the simulation. If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- x, y The initial stoichiometries that give the desired initial state of charge """ - esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value) + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_initial_stoichiometries(initial_value) def get_min_max_stoichiometries( - parameter_values, param=None, known_value="cyclable lithium capacity" + parameter_values, param=None, known_value="cyclable lithium capacity", options=None ): """ Calculate min/max stoichiometries @@ -584,16 +885,93 @@ def get_min_max_stoichiometries( param : :class:`pybamm.LithiumIonParameters`, optional The symbolic parameter set to use for the simulation. If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- x_0, x_100, y_100, y_0 The min/max stoichiometries """ - esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value) + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_min_max_stoichiometries() +def get_initial_ocps( + initial_value, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, +): + """ + Calculate initial open-circuit potentials to start off the simulation at a + particular state of charge, given voltage limits, open-circuit potentials, etc + defined by parameter_values + + Parameters + ---------- + initial_value : float + Target initial value. + If integer, interpreted as SOC, must be between 0 and 1. + If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max. + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial stoichiometries. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + + Returns + ------- + Un, Up + The initial electrode OCPs that give the desired initial state of charge + """ + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) + return esoh_solver.get_initial_ocps(initial_value) + + +def get_min_max_ocps( + parameter_values, param=None, known_value="cyclable lithium capacity", options=None +): + """ + Calculate min/max open-circuit potentials + given voltage limits, open-circuit potentials, etc defined by parameter_values + + Parameters + ---------- + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial open-circuit potentials. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + + Returns + ------- + Un_0, Un_100, Up_100, Up_0 + The min/max OCPs + """ + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) + return esoh_solver.get_min_max_ocps() + + def theoretical_energy_integral(parameter_values, inputs, points=100): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc @@ -608,7 +986,6 @@ def theoretical_energy_integral(parameter_values, inputs, points=100): electrodes, respectively points : int The number of points at which to calculate voltage. - Returns ------- E @@ -657,7 +1034,6 @@ def calculate_theoretical_energy( The soc at end of discharge, default 0.0 points : int The number of points at which to calculate voltage. - Returns ------- E @@ -673,3 +1049,33 @@ def calculate_theoretical_energy( points=points, ) return E + + +def _get_msmr_potential_model(parameter_values, param): + """ + Returns a solver to calculate the open-circuit potentials of the individual + electrodes at the given stoichiometries + """ + V_max = param.voltage_high_cut + V_min = param.voltage_low_cut + x_n = param.n.prim.x + x_p = param.p.prim.x + model = pybamm.BaseModel() + Un = pybamm.Variable("Un") + Up = pybamm.Variable("Up") + x = pybamm.InputParameter("x") + y = pybamm.InputParameter("y") + model.algebraic = { + Un: x_n(Un) - x, + Up: x_p(Up) - y, + } + model.initial_conditions = { + Un: 1 - x, + Up: V_max * (1 - y) + V_min * y, + } + model.variables = { + "Un": Un, + "Up": Up, + } + parameter_values.process_model(model) + return model diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py index ed55a2d621..39aad1c896 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py @@ -37,8 +37,8 @@ def __init__(self, working_electrode, name="Electrode-specific SOH model"): U_w = param.p.prim.U Q = Q_w * (x_100 - x_0) - V_max = param.opc_soc_100_dimensional - V_min = param.opc_soc_0_dimensional + V_max = param.ocp_soc_100_dimensional + V_min = param.ocp_soc_0_dimensional self.algebraic = { x_100: U_w(x_100, T_ref) - V_max, diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py new file mode 100644 index 0000000000..3ca07c4ef8 --- /dev/null +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py @@ -0,0 +1,49 @@ +import pybamm +from .dfn import DFN + + +class MSMR(DFN): + def __init__(self, options=None, name="MSMR", build=True): + # Necessary/default options + options = options or {} + if "number of MSMR reactions" not in options: + raise pybamm.OptionError( + "number of MSMR reactions must be specified for MSMR" + ) + if ( + "open-circuit potential" in options + and options["open-circuit potential"] != "MSMR" + ): + raise pybamm.OptionError( + "'open-circuit potential' must be 'MSMR' for MSMR not '{}'".format( + options["open-circuit potential"] + ) + ) + elif "particle" in options and options["particle"] == "MSMR": + raise pybamm.OptionError( + "'particle' must be 'MSMR' for MSMR not '{}'".format( + options["particle"] + ) + ) + elif ( + "intercalation kinetics" in options + and options["intercalation kinetics"] == "MSMR" + ): + raise pybamm.OptionError( + "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format( + options["intercalation kinetics"] + ) + ) + else: + options.update( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + } + ) + super().__init__(options=options, name=name) + + @property + def default_parameter_values(self): + return pybamm.ParameterValues("MSMR_Example") diff --git a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py index e1717600b8..a704bd0b33 100644 --- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py +++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py @@ -48,6 +48,10 @@ def set_particle_submodel(self): submod = pybamm.particle.XAveragedPolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=True + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py index 90d1262763..e54a7ec646 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/spm.py @@ -105,6 +105,10 @@ def set_particle_submodel(self): submod = pybamm.particle.XAveragedPolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=True + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/submodels/interface/kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/__init__.py index c8b8552574..d99ec56783 100644 --- a/pybamm/models/submodels/interface/kinetics/__init__.py +++ b/pybamm/models/submodels/interface/kinetics/__init__.py @@ -5,7 +5,7 @@ from .marcus import Marcus, MarcusHushChidsey from .tafel import ForwardTafel # , BackwardTafel from .no_reaction import NoReaction - +from .msmr_butler_volmer import MSMRButlerVolmer from .diffusion_limited import DiffusionLimited from .inverse_kinetics.inverse_butler_volmer import ( InverseButlerVolmer, diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index 9b37467894..c6cdc94ec3 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -78,10 +78,24 @@ def get_coupled_variables(self, variables): ): delta_phi = pybamm.PrimaryBroadcast(delta_phi, [f"{domain} particle size"]) - # Get exchange-current density - j0 = self._get_exchange_current_density(variables) + # Get exchange-current density. For MSMR models we calculate the exchange + # current density for each reaction, then sum these to give a total exchange + # current density. Note: this is only used for the "exchange current density" + # variables. For the interfacial current density variables, we sum the + # interfacial currents from each reaction. + if domain_options["intercalation kinetics"] == "MSMR": + N = int(domain_options["number of MSMR reactions"]) + j0 = 0 + for i in range(N): + j0_j = self._get_exchange_current_density_by_reaction(variables, i) + variables.update( + self._get_standard_exchange_current_by_reaction_variables(j0_j, i) + ) + j0 += j0_j + else: + j0 = self._get_exchange_current_density(variables) - # Get open-circuit potential + # Get open-circuit potential variables and reaction overpotential if ( domain_options["particle size"] == "distribution" and self.options.electrode_types[domain] == "porous" @@ -171,7 +185,17 @@ def get_coupled_variables(self, variables): # Update j, except in the "distributed SEI resistance" model, where j will be # found by solving an algebraic equation. # (In the "distributed SEI resistance" model, we have already defined j) - j = self._get_kinetics(j0, ne, eta_r, T, u) + # For MSMR model we calculate the total current density by summing the current + # densities from each reaction + if domain_options["intercalation kinetics"] == "MSMR": + j = 0 + for i in range(N): + j0_j = self._get_exchange_current_density_by_reaction(variables, i) + j_j = self._get_kinetics_by_reaction(j0_j, ne, eta_r, T, u, i) + variables.update(self._get_standard_icd_by_reaction_variables(j_j, i)) + j += j_j + else: + j = self._get_kinetics(j0, ne, eta_r, T, u) if j.domain == [f"{domain} particle size"]: # If j depends on particle size, get size-dependent "distribution" diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py new file mode 100644 index 0000000000..6a4b9f5023 --- /dev/null +++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py @@ -0,0 +1,152 @@ +# +# Bulter volmer class for the MSMR formulation +# + +import pybamm +from .base_kinetics import BaseKinetics + + +class MSMRButlerVolmer(BaseKinetics): + """ + Submodel which implements the forward Butler-Volmer equation in the MSMR + formulation in which the interfacial current density is summed over all + reactions. + + Parameters + ---------- + param : parameter class + model parameters + domain : str + The domain to implement the model, either: 'Negative' or 'Positive'. + reaction : str + The name of the reaction being implemented + options: dict + A dictionary of options to be passed to the model. + See :class:`pybamm.BaseBatteryModel` + phase : str, optional + Phase of the particle (default is "primary") + """ + + def __init__(self, param, domain, reaction, options, phase="primary"): + super().__init__(param, domain, reaction, options, phase) + + def _get_exchange_current_density_by_reaction(self, variables, index): + """ " + A private function to obtain the exchange current density for each reaction + in the MSMR formulation. + + Parameters + ---------- + variables: dict + The variables in the full model. + + Returns + ------- + j0 : :class: `pybamm.Symbol` + The exchange current density. + """ + phase_param = self.phase_param + domain, Domain = self.domain_Domain + + c_e = variables[f"{Domain} electrolyte concentration [mol.m-3]"] + T = variables[f"{Domain} electrode temperature [K]"] + + if self.reaction == "lithium-ion main": + # For "particle-size distribution" submodels, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + ocp = variables[ + f"{Domain} electrode open-circuit potential distribution [V]" + ] + # If all variables were broadcast (in "x"), take only the orphans, + # then re-broadcast c_e + if ( + isinstance(ocp, pybamm.Broadcast) + and isinstance(c_e, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + ocp = ocp.orphans[0] + c_e = c_e.orphans[0] + T = T.orphans[0] + + # as c_e must now be a scalar, re-broadcast to + # "current collector" + c_e = pybamm.PrimaryBroadcast(c_e, ["current collector"]) + # broadcast c_e, T onto "particle size" + c_e = pybamm.PrimaryBroadcast(c_e, [f"{domain} particle size"]) + T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) + + else: + ocp = variables[f"{Domain} electrode open-circuit potential [V]"] + # If all variables were broadcast, take only the orphans + if ( + isinstance(ocp, pybamm.Broadcast) + and isinstance(c_e, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + ocp = ocp.orphans[0] + c_e = c_e.orphans[0] + T = T.orphans[0] + + j0 = phase_param.j0_j(c_e, ocp, T, index) + + return j0 + + def _get_standard_exchange_current_by_reaction_variables(self, j0, index): + domain = self.domain + # Size average. For j0 variables that depend on particle size, see + # "_get_standard_size_distribution_exchange_current_variables" + if j0.domain in [["negative particle size"], ["positive particle size"]]: + j0 = pybamm.size_average(j0) + # Average, and broadcast if necessary + j0_av = pybamm.x_average(j0) + + # X-average, and broadcast if necessary + if j0.domain == []: + j0 = pybamm.FullBroadcast(j0, f"{domain} electrode", "current collector") + elif j0.domain == ["current collector"]: + j0 = pybamm.PrimaryBroadcast(j0, f"{domain} electrode") + + d = domain[0] + variables = { + f"j0_{d}_{index} [A.m-2]": j0, + f"X-averaged j0_{d}_{index} [A.m-2]": j0_av, + } + + return variables + + def _get_kinetics_by_reaction(self, j0, ne, eta_r, T, u, index): + alpha = self.phase_param.alpha_bv_j(index) + Feta_RT = self.param.F * eta_r / (self.param.R * T) + return ( + u + * j0 + * ( + pybamm.exp(ne * (1 - alpha) * Feta_RT) + - pybamm.exp(-ne * alpha * Feta_RT) + ) + ) + + def _get_standard_icd_by_reaction_variables(self, j, index): + domain = self.domain + j.print_name = f"j_{domain[0]}" + + # Size average. For j variables that depend on particle size, see + # "_get_standard_size_distribution_interfacial_current_variables" + if j.domain in [["negative particle size"], ["positive particle size"]]: + j = pybamm.size_average(j) + # Average, and broadcast if necessary + j_av = pybamm.x_average(j) + if j.domain == []: + j = pybamm.FullBroadcast(j, f"{domain} electrode", "current collector") + elif j.domain == ["current collector"]: + j = pybamm.PrimaryBroadcast(j, f"{domain} electrode") + + d = domain[0] + variables = { + f"j_{d}_{index} [A.m-2]": j, + f"X-averaged j_{d}_{index} [A.m-2]": j_av, + } + + return variables diff --git a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py index d644b87e76..5f8a409bba 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py @@ -1,3 +1,4 @@ from .base_ocp import BaseOpenCircuitPotential from .single_ocp import SingleOpenCircuitPotential from .current_sigmoid_ocp import CurrentSigmoidOpenCircuitPotential +from .msmr_ocp import MSMROpenCircuitPotential diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py new file mode 100644 index 0000000000..2ac87279f2 --- /dev/null +++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py @@ -0,0 +1,63 @@ +# +# Open-circuit potential from the Multi-Species Multi-Reaction framework +# +import pybamm +from . import BaseOpenCircuitPotential + + +class MSMROpenCircuitPotential(BaseOpenCircuitPotential): + """ + Class for open-circuit potential within the Multi-Species Multi-Reaction + framework :footcite:t:`Baker2018`. The thermodynamic model is presented in + :footcite:t:`Verbrugge2017`, along with parameter values for a number of + substitutional materials. + """ + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.reaction == "lithium-ion main": + T = variables[f"{Domain} electrode temperature [K]"] + # For "particle-size distribution" models, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry distribution" + ] + ocp_surf = variables[ + f"{Domain} {phase_name}particle surface potential distribution [V]" + ] + # If variable was broadcast, take only the orphan + if ( + isinstance(sto_surf, pybamm.Broadcast) + and isinstance(ocp_surf, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + sto_surf = sto_surf.orphans[0] + ocp_surf = ocp_surf.orphans[0] + T = T.orphans[0] + T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) + else: + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry" + ] + ocp_surf = variables[ + f"{Domain} {phase_name}particle surface potential [V]" + ] + # If variable was broadcast, take only the orphan + if ( + isinstance(sto_surf, pybamm.Broadcast) + and isinstance(ocp_surf, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + sto_surf = sto_surf.orphans[0] + ocp_surf = ocp_surf.orphans[0] + T = T.orphans[0] + + ocp_bulk = variables[f"Average {domain} {phase_name}particle potential [V]"] + dUdT = self.phase_param.dUdT(sto_surf) + + variables.update(self._get_standard_ocp_variables(ocp_surf, ocp_bulk, dUdT)) + return variables diff --git a/pybamm/models/submodels/particle/__init__.py b/pybamm/models/submodels/particle/__init__.py index 7f3c19953d..237b2c19c8 100644 --- a/pybamm/models/submodels/particle/__init__.py +++ b/pybamm/models/submodels/particle/__init__.py @@ -3,3 +3,4 @@ from .polynomial_profile import PolynomialProfile from .x_averaged_polynomial_profile import XAveragedPolynomialProfile from .total_particle_concentration import TotalConcentration +from .msmr_diffusion import MSMRDiffusion diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 01188206c7..ad751c3911 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -49,7 +49,7 @@ def _get_effective_diffusivity(self, c, T, current): D_delith = phase_param.D(c, T, "delithiation") D = m_lith * D_lith + m_delith * D_delith - # Account for stress-induced difftusion by defining a multiplicative + # Account for stress-induced diffusion by defining a multiplicative # "stress factor" stress_option = getattr(self.options, domain)["stress-induced diffusion"] @@ -71,7 +71,7 @@ def _get_standard_concentration_variables( """ All particle submodels must provide the particle concentration as an argument to this method. Some submodels solve for quantities other than the concentration - itself, for example the 'XAveragedFickianDiffusion' models solves for the + itself, for example the 'XAveragedPolynomialProfile' models solves for the x-averaged concentration. In such cases the variables being solved for (set in 'get_fundamental_variables') must also be passed as keyword arguments. If not passed as keyword arguments, the various average concentrations and surface @@ -98,44 +98,64 @@ def _get_standard_concentration_variables( c_s_av = pybamm.r_average(c_s_xav) variables = { - f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale, - f"{Domain} {phase_name}particle concentration": c_s / c_scale, + # Dimensional concentration f"{Domain} {phase_name}particle concentration [mol.m-3]": c_s, - f"X-averaged {domain} {phase_name}particle concentration": c_s_xav - / c_scale, f"X-averaged {domain} {phase_name}particle " "concentration [mol.m-3]": c_s_xav, - f"R-averaged {domain} {phase_name}particle concentration": c_s_rav - / c_scale, f"R-averaged {domain} {phase_name}particle " "concentration [mol.m-3]": c_s_rav, - f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale, f"Average {domain} {phase_name}particle concentration [mol.m-3]": c_s_av, - f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale, - f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale, f"{Domain} {phase_name}particle surface concentration [mol.m-3]": c_s_surf, f"X-averaged {domain} {phase_name}particle " - "surface concentration": c_s_surf_av / c_scale, - f"X-averaged {domain} {phase_name}particle " "surface concentration [mol.m-3]": c_s_surf_av, - f"{Domain} electrode extent of lithiation": c_s_rav / c_scale, - f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale, - f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s) - / c_scale, - f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s) - / c_scale, f"Minimum {domain} {phase_name}particle concentration [mol.m-3]" "": pybamm.min(c_s), f"Maximum {domain} {phase_name}particle concentration [mol.m-3]" "": pybamm.max(c_s), f"Minimum {domain} {phase_name}particle " + f"Minimum {domain} {phase_name}particle " + "surface concentration [mol.m-3]": pybamm.min(c_s_surf), + f"Maximum {domain} {phase_name}particle " + "surface concentration [mol.m-3]": pybamm.max(c_s_surf), + # Dimensionless concentration + f"{Domain} {phase_name}particle concentration": c_s / c_scale, + f"X-averaged {domain} {phase_name}particle concentration": c_s_xav + / c_scale, + f"R-averaged {domain} {phase_name}particle concentration": c_s_rav + / c_scale, + f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale, + f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale, + f"X-averaged {domain} {phase_name}particle " + "surface concentration": c_s_surf_av / c_scale, + f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s) + / c_scale, + f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s) + / c_scale, + f"Minimum {domain} {phase_name}particle " "surface concentration": pybamm.min(c_s_surf) / c_scale, f"Maximum {domain} {phase_name}particle " "surface concentration": pybamm.max(c_s_surf) / c_scale, + # Stoichiometry (equivalent to dimensionless concentration) + f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale, + f"X-averaged {domain} {phase_name}particle stoichiometry": c_s_xav + / c_scale, + f"R-averaged {domain} {phase_name}particle stoichiometry": c_s_rav + / c_scale, + f"Average {domain} {phase_name}particle stoichiometry": c_s_av / c_scale, + f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale, + f"X-averaged {domain} {phase_name}particle " + "surface stoichiometry": c_s_surf_av / c_scale, + f"Minimum {domain} {phase_name}particle stoichiometry": pybamm.min(c_s) + / c_scale, + f"Maximum {domain} {phase_name}particle stoichiometry": pybamm.max(c_s) + / c_scale, f"Minimum {domain} {phase_name}particle " - "surface concentration [mol.m-3]": pybamm.min(c_s_surf), + "surface stoichiometry": pybamm.min(c_s_surf) / c_scale, f"Maximum {domain} {phase_name}particle " - "surface concentration [mol.m-3]": pybamm.max(c_s_surf), + "surface stoichiometry": pybamm.max(c_s_surf) / c_scale, + # Electrode extent of lithiation + f"{Domain} electrode extent of lithiation": c_s_rav / c_scale, + f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale, } return variables @@ -302,7 +322,7 @@ def _get_standard_concentration_distribution_variables(self, c_s): c_s_surf_xav_distribution, [f"{domain} {phase_name}particle"] ) - # Concentration distribution in all domains. + # Concentration distribution in all domains c_s_distribution = pybamm.PrimaryBroadcast( c_s_surf_distribution, [f"{domain} {phase_name}particle"] ) @@ -328,32 +348,49 @@ def _get_standard_concentration_distribution_variables(self, c_s): c_s_av_distribution = pybamm.x_average(c_s_rav_distribution) variables = { - f"Average {domain} {phase_name}particle concentration " - "distribution": c_s_av_distribution / c_scale, - f"Average {domain} {phase_name}particle concentration " - "distribution [mol.m-3]": c_s_av_distribution, - f"{Domain} {phase_name}particle concentration " - "distribution": c_s_distribution / c_scale, + # Dimensional concentration f"{Domain} {phase_name}particle concentration distribution " "[mol.m-3]": c_s_distribution, - f"R-averaged {domain} {phase_name}particle concentration " - "distribution": c_s_rav_distribution / c_scale, - f"R-averaged {domain} {phase_name}particle concentration distribution " - "[mol.m-3]": c_s_rav_distribution, - f"X-averaged {domain} {phase_name}particle concentration " - "distribution": c_s_xav_distribution / c_scale, f"X-averaged {domain} {phase_name}particle concentration distribution " "[mol.m-3]": c_s_xav_distribution, - f"X-averaged {domain} {phase_name}particle surface concentration" - " distribution": c_s_surf_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle concentration distribution " + "[mol.m-3]": c_s_rav_distribution, + f"Average {domain} {phase_name}particle concentration " + "distribution [mol.m-3]": c_s_av_distribution, + f"{Domain} {phase_name}particle surface concentration" + " distribution [mol.m-3]": c_s_surf_distribution, f"X-averaged {domain} {phase_name}particle surface concentration " "distribution [mol.m-3]": c_s_surf_xav_distribution, + # Dimensionless concentration + f"{Domain} {phase_name}particle concentration " + "distribution": c_s_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle concentration " + "distribution": c_s_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle concentration " + "distribution": c_s_rav_distribution / c_scale, + f"Average {domain} {phase_name}particle concentration " + "distribution": c_s_av_distribution / c_scale, f"{Domain} {phase_name}particle surface concentration" " distribution": c_s_surf_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle surface concentration" + " distribution": c_s_surf_xav_distribution / c_scale, + # Stoichiometry (equivalent to dimensionless concentration) + f"{Domain} {phase_name}particle stoichiometry " + "distribution": c_s_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle stoichiometry " + "distribution": c_s_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle stoichiometry " + "distribution": c_s_rav_distribution / c_scale, + f"Average {domain} {phase_name}particle stoichiometry " + "distribution": c_s_av_distribution / c_scale, f"{Domain} {phase_name}particle surface stoichiometry" " distribution": c_s_surf_distribution / c_scale, - f"{Domain} {phase_name}particle surface concentration" - " distribution [mol.m-3]": c_s_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface stoichiometry" + " distribution": c_s_surf_xav_distribution / c_scale, + # Electrode extent of lithiation + f"{Domain} electrode extent of lithiation": c_s_rav_distribution / c_scale, + f"X-averaged {domain} electrode extent of lithiation": c_s_av_distribution + / c_scale, } return variables diff --git a/pybamm/models/submodels/particle/fickian_diffusion.py b/pybamm/models/submodels/particle/fickian_diffusion.py index 7707284a36..31c5e6be6c 100644 --- a/pybamm/models/submodels/particle/fickian_diffusion.py +++ b/pybamm/models/submodels/particle/fickian_diffusion.py @@ -122,6 +122,7 @@ def get_fundamental_variables(self): if self.x_average is True: c_s = pybamm.SecondaryBroadcast(c_s, [f"{domain} electrode"]) + # Standard concentration variables (size-independent) variables.update(self._get_standard_concentration_variables(c_s)) return variables @@ -169,7 +170,6 @@ def get_coupled_variables(self, variables): f"{Domain} {phase_name}particle " "concentration distribution [mol.m-3]" ] - # broadcast T to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( variables[f"{Domain} electrode temperature [K]"], @@ -185,7 +185,6 @@ def get_coupled_variables(self, variables): f"X-averaged {domain} {phase_name}particle " "concentration distribution [mol.m-3]" ] - # broadcast to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( variables[f"X-averaged {domain} electrode temperature [K]"], @@ -207,7 +206,7 @@ def get_coupled_variables(self, variables): 1 / (R_broad_nondim**2) ) * pybamm.div(N_s), - f"{Domain} {phase_name}particle bc [mol.m-2]": -j + f"{Domain} {phase_name}particle bc [mol.m-4]": -j * R_nondim / param.F / pybamm.surf(D_eff), @@ -286,7 +285,7 @@ def set_boundary_conditions(self, variables): "concentration distribution [mol.m-3]" ] - rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-2]"] + rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-4]"] self.boundary_conditions = { c_s: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")} } diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py new file mode 100644 index 0000000000..65ab913e97 --- /dev/null +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -0,0 +1,665 @@ +# +# Class for particles using the MSMR model +# +import pybamm +from .base_particle import BaseParticle + + +class MSMRDiffusion(BaseParticle): + """ + Class for molar conservation in particles within the Multi-Species Multi-Reaction + framework :footcite:t:`Baker2018`. The thermodynamic model is presented in + :footcite:t:`Verbrugge2017`, along with parameter values for a number of + substitutional materials. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + domain : str + The domain of the model either 'Negative' or 'Positive' + options: dict + A dictionary of options to be passed to the model. + See :class:`pybamm.BaseBatteryModel` + phase : str, optional + Phase of the particle (default is "primary") + x_average : bool + Whether the particle concentration is averaged over the x-direction + """ + + def __init__(self, param, domain, options, phase="primary", x_average=False): + super().__init__(param, domain, options, phase) + self.x_average = x_average + + pybamm.citations.register("Baker2018") + pybamm.citations.register("Verbrugge2017") + + def get_fundamental_variables(self): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + variables = {} + + # Define "particle" potential variables. In the MSMR model, we solve for the + # potential as a function of position within the electrode and particles (and + # particle-size distribution, if applicable). The potential is then used to + # calculate the stoichiometry, which is used to calculate the particle + # concentration. + c_max = self.phase_param.c_max + if self.size_distribution is False: + if self.x_average is False: + U = pybamm.Variable( + f"{Domain} {phase_name}particle potential [V]", + f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} electrode", + "tertiary": "current collector", + }, + ) + U.print_name = f"U_{domain[0]}" + else: + U_xav = pybamm.Variable( + f"X-averaged {domain} {phase_name}particle " "potential [V]", + f"{domain} {phase_name}particle", + auxiliary_domains={"secondary": "current collector"}, + ) + U_xav.print_name = f"U_{domain[0]}_xav" + U = pybamm.SecondaryBroadcast(U_xav, f"{domain} electrode") + else: + if self.x_average is False: + U_distribution = pybamm.Variable( + f"{Domain} {phase_name}particle " "potential distribution [V]", + domain=f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} {phase_name}particle size", + "tertiary": f"{domain} electrode", + "quaternary": "current collector", + }, + ) + R = pybamm.SpatialVariable( + f"R_{domain[0]}", + domain=[f"{domain} {phase_name}particle size"], + auxiliary_domains={ + "secondary": f"{domain} electrode", + "tertiary": "current collector", + }, + coord_sys="cartesian", + ) + variables = self._get_distribution_variables(R) + f_v_dist = variables[ + f"{Domain} volume-weighted {phase_name}" + "particle-size distribution [m-1]" + ] + else: + U_distribution = pybamm.Variable( + f"X-averaged {domain} {phase_name}particle " + "potential distribution [V]", + domain=f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + R = pybamm.SpatialVariable( + f"R_{domain[0]}", + domain=[f"{domain} {phase_name}particle size"], + auxiliary_domains={"secondary": "current collector"}, + coord_sys="cartesian", + ) + variables = self._get_distribution_variables(R) + f_v_dist = variables[ + f"X-averaged {domain} volume-weighted {phase_name}" + "particle-size distribution [m-1]" + ] + + # Standard potential distribution_variables + variables.update( + self._get_standard_potential_distribution_variables(U_distribution) + ) + + # Calculate the stoichiometry distribution from the potential distribution + x_distribution = self.phase_param.x(U_distribution) + dxdU_distribution = self.phase_param.dxdU(U_distribution) + + # Standard stoichiometry and concentration distribution variables + # (size-dependent) + c_s_distribution = x_distribution * c_max + variables.update( + self._get_standard_concentration_distribution_variables( + c_s_distribution + ) + ) + variables.update( + self._get_standard_differential_stoichiometry_distribution_variables( + dxdU_distribution + ) + ) + + # Standard size-averaged variables. Average potentials using + # the volume-weighted distribution since they are volume-based + # quantities. Necessary for output variables "Total lithium in + # negative electrode [mol]", etc, to be calculated correctly + U = pybamm.Integral(f_v_dist * U_distribution, R) + if self.x_average is True: + U = pybamm.SecondaryBroadcast(U, [f"{domain} electrode"]) + + # Standard potential variables + variables.update(self._get_standard_potential_variables(U)) + + # Standard fractional occupancy variables (these are indexed by reaction number) + variables.update(self._get_standard_fractional_occupancy_variables(U)) + variables.update( + self._get_standard_differential_fractional_occupancy_variables(U) + ) + + # Calculate the (total) stoichiometry from the potential + x = self.phase_param.x(U) + dxdU = self.phase_param.dxdU(U) + + # Standard (total) stoichiometry and concentration variables (size-independent) + c_s = x * c_max + variables.update(self._get_standard_concentration_variables(c_s)) + variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) + + return variables + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + param = self.param + + if self.size_distribution is False: + if self.x_average is False: + x = variables[f"{Domain} {phase_name}particle stoichiometry"] + dxdU = variables[ + f"{Domain} {phase_name}particle differential stoichiometry [V-1]" + ] + U = variables[f"{Domain} {phase_name}particle potential [V]"] + T = pybamm.PrimaryBroadcast( + variables[f"{Domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle"], + ) + R_nondim = variables[f"{Domain} {phase_name}particle radius"] + j = variables[ + f"{Domain} electrode {phase_name}" + "interfacial current density [A.m-2]" + ] + else: + x = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] + dxdU = variables[ + f"X-averaged {domain} {phase_name}particle differential " + "stoichiometry [V-1]" + ] + U = variables[ + f"X-averaged {domain} {phase_name}particle " "potential [V]" + ] + T = pybamm.PrimaryBroadcast( + variables[f"X-averaged {domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle"], + ) + R_nondim = 1 + j = variables[ + f"X-averaged {domain} electrode {phase_name}" + "interfacial current density [A.m-2]" + ] + R_broad_nondim = R_nondim + else: + R_nondim = variables[f"{Domain} {phase_name}particle sizes"] + R_broad_nondim = pybamm.PrimaryBroadcast( + R_nondim, [f"{domain} {phase_name}particle"] + ) + if self.x_average is False: + x = variables[ + f"{Domain} {phase_name}particle stoichiometry distribution" + ] + dxdU = variables[ + f"{Domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]" + ] + U = variables[ + f"{Domain} {phase_name}particle potential " "distribution [V]" + ] + # broadcast T to "particle size" domain then again into "particle" + T = pybamm.PrimaryBroadcast( + variables[f"{Domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle size"], + ) + T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"]) + j = variables[ + f"{Domain} electrode {phase_name}interfacial " + "current density distribution [A.m-2]" + ] + else: + x = variables[ + f"X-averaged {domain} {phase_name}particle " + "stoichiometry distribution" + ] + dxdU = variables[ + f"X-averaged {domain} {phase_name}particle " + "differential stoichiometry distribution [V-1]" + ] + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "potential distribution [V]" + ] + # broadcast to "particle size" domain then again into "particle" + T = pybamm.PrimaryBroadcast( + variables[f"X-averaged {domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle size"], + ) + T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"]) + j = variables[ + f"X-averaged {domain} electrode {phase_name}interfacial " + "current density distribution [A.m-2]" + ] + + # Note: diffusivity is given as a function of concentration here, + # not stoichiometry + c_max = self.phase_param.c_max + current = variables["Total current density [A.m-2]"] + D_eff = self._get_effective_diffusivity(x * c_max, T, current) + f = self.param.F / (self.param.R * T) + N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) + variables.update( + { + f"{Domain} {phase_name}particle rhs [V.s-1]": -( + 1 / (R_broad_nondim**2) + ) + * pybamm.div(N_s) + / c_max + / dxdU, + f"{Domain} {phase_name}particle bc [V.m-1]": j + * R_nondim + / param.F + / pybamm.surf(c_max * x * (1 - x) * f * D_eff), + } + ) + + if self.size_distribution is True: + # Size-dependent flux variables + variables.update( + self._get_standard_diffusivity_distribution_variables(D_eff) + ) + variables.update(self._get_standard_flux_distribution_variables(N_s)) + # Size-averaged flux variables + R = variables[f"{Domain} {phase_name}particle sizes [m]"] + f_a_dist = self.phase_param.f_a_dist(R) + D_eff = pybamm.Integral(f_a_dist * D_eff, R) + N_s = pybamm.Integral(f_a_dist * N_s, R) + + if self.x_average is True: + D_eff = pybamm.SecondaryBroadcast(D_eff, [f"{domain} electrode"]) + N_s = pybamm.SecondaryBroadcast(N_s, [f"{domain} electrode"]) + + variables.update(self._get_standard_diffusivity_variables(D_eff)) + variables.update(self._get_standard_flux_variables(N_s)) + + return variables + + def set_rhs(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.size_distribution is False: + if self.x_average is False: + U = variables[f"{Domain} {phase_name}particle potential [V]"] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " "potential [V]" + ] + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " "potential distribution [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "potential distribution [V]" + ] + self.rhs = {U: variables[f"{Domain} {phase_name}particle rhs [V.s-1]"]} + + def set_boundary_conditions(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.size_distribution is False: + if self.x_average is False: + U = variables[f"{Domain} {phase_name}particle potential [V]"] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " "potential [V]" + ] + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " "potential distribution [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "potential distribution [V]" + ] + + rbc = variables[f"{Domain} {phase_name}particle bc [V.m-1]"] + self.boundary_conditions = { + U: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")} + } + + def set_initial_conditions(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + U_init = self.phase_param.U_init + if self.size_distribution is False: + if self.x_average is False: + U = variables[f"{Domain} {phase_name}particle potential [V]"] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " "potential [V]" + ] + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " "potential distribution [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "potential distribution [V]" + ] + self.initial_conditions = {U: U_init} + + def _get_standard_potential_variables(self, U): + """ + A private function to obtain the standard variables which can be derived from + the potential. + """ + domain, Domain = self.domain_Domain + phase_name = self.phase_name + U_surf = pybamm.surf(U) + U_surf_av = pybamm.x_average(U_surf) + U_xav = pybamm.x_average(U) + U_rav = pybamm.r_average(U) + U_av = pybamm.r_average(U_xav) + variables = { + f"{Domain} {phase_name}particle potential [V]": U, + f"X-averaged {domain} {phase_name}particle " "potential [V]": U_xav, + f"R-averaged {domain} {phase_name}particle " "potential [V]": U_rav, + f"Average {domain} {phase_name}particle potential [V]": U_av, + f"{Domain} {phase_name}particle surface potential [V]": U_surf, + f"X-averaged {domain} {phase_name}particle " + "surface potential [V]": U_surf_av, + f"Minimum {domain} {phase_name}particle potential [V]" "": pybamm.min(U), + f"Maximum {domain} {phase_name}particle potential [V]" "": pybamm.max(U), + f"Minimum {domain} {phase_name}particle " + "surface potential [V]": pybamm.min(U_surf), + f"Maximum {domain} {phase_name}particle " + "surface potential [V]": pybamm.max(U_surf), + } + return variables + + def _get_standard_potential_distribution_variables(self, U): + """ + A private function to obtain the standard variables which can be derived from + the potential distribution in particle size. + """ + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + # Broadcast and x-average when necessary + if U.domain == [f"{domain} {phase_name}particle size"] and U.domains[ + "secondary" + ] != [f"{domain} electrode"]: + # X-avg potential distribution + U_xav_distribution = pybamm.PrimaryBroadcast( + U, [f"{domain} {phase_name}particle"] + ) + + # Surface potential distribution variables + U_surf_xav_distribution = U + U_surf_distribution = pybamm.SecondaryBroadcast( + U_surf_xav_distribution, [f"{domain} electrode"] + ) + + # potential distribution in all domains. + U_distribution = pybamm.PrimaryBroadcast( + U_surf_distribution, [f"{domain} {phase_name}particle"] + ) + elif U.domain == [f"{domain} {phase_name}particle"] and ( + U.domains["tertiary"] != [f"{domain} electrode"] + ): + # X-avg potential distribution + U_xav_distribution = U + + # Surface potential distribution variables + U_surf_xav_distribution = pybamm.surf(U_xav_distribution) + U_surf_distribution = pybamm.SecondaryBroadcast( + U_surf_xav_distribution, [f"{domain} electrode"] + ) + + # potential distribution in all domains + U_distribution = pybamm.TertiaryBroadcast( + U_xav_distribution, [f"{domain} electrode"] + ) + elif U.domain == [f"{domain} {phase_name}particle size"] and U.domains[ + "secondary" + ] == [f"{domain} electrode"]: + # Surface potential distribution variables + U_surf_distribution = U + U_surf_xav_distribution = pybamm.x_average(U) + + # X-avg potential distribution + U_xav_distribution = pybamm.PrimaryBroadcast( + U_surf_xav_distribution, [f"{domain} {phase_name}particle"] + ) + + # potential distribution in all domains + U_distribution = pybamm.PrimaryBroadcast( + U_surf_distribution, [f"{domain} {phase_name}particle"] + ) + else: + U_distribution = U + + # x-average the *tertiary* domain. + # NOTE: not yet implemented. Make 0.5 everywhere + U_xav_distribution = pybamm.FullBroadcast( + 0.5, + [f"{domain} {phase_name}particle"], + { + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + + # Surface potential distribution variables + U_surf_distribution = pybamm.surf(U) + U_surf_xav_distribution = pybamm.x_average(U_surf_distribution) + + U_rav_distribution = pybamm.r_average(U_distribution) + U_av_distribution = pybamm.x_average(U_rav_distribution) + + variables = { + f"{Domain} {phase_name}particle potential distribution [V]": U_distribution, + f"X-averaged {domain} {phase_name}particle potential " + "distribution [V]": U_xav_distribution, + f"R-averaged {domain} {phase_name}particle potential " + "distribution [V]": U_rav_distribution, + f"Average {domain} {phase_name}particle potential " + "distribution [V]": U_av_distribution, + f"{Domain} {phase_name}particle surface potential" + " distribution [V]": U_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface potential " + "distribution [V]": U_surf_xav_distribution, + } + return variables + + def _get_standard_fractional_occupancy_variables(self, U): + options = self.options + domain = self.domain + d = domain[0] + variables = {} + # Loop over all reactions + N = int(getattr(options, domain)["number of MSMR reactions"]) + for i in range(N): + x = self.phase_param.x_j(U, i) + x_surf = pybamm.surf(x) + x_surf_av = pybamm.x_average(x_surf) + x_xav = pybamm.x_average(x) + x_rav = pybamm.r_average(x) + x_av = pybamm.r_average(x_xav) + variables.update( + { + f"x_{d}_{i}": x, + f"X-averaged x_{d}_{i}": x_xav, + f"R-averaged x_{d}_{i}": x_rav, + f"Average x_{d}_{i}": x_av, + f"Surface x_{d}_{i}": x_surf, + f"X-averaged surface x_{d}_{i}": x_surf_av, + } + ) + return variables + + def _get_standard_differential_fractional_occupancy_variables(self, U): + options = self.options + domain = self.domain + d = domain[0] + variables = {} + # Loop over all reactions + N = int(getattr(options, domain)["number of MSMR reactions"]) + for i in range(N): + dxdU = self.phase_param.dxdU_j(U, i) + dxdU_surf = pybamm.surf(dxdU) + dxdU_surf_av = pybamm.x_average(dxdU_surf) + dxdU_xav = pybamm.x_average(dxdU) + dxdU_rav = pybamm.r_average(dxdU) + dxdU_av = pybamm.r_average(dxdU_xav) + variables.update( + { + f"dxdU_{d}_{i}": dxdU, + f"X-averaged dxdU_{d}_{i}": dxdU_xav, + f"R-averaged dxdU_{d}_{i}": dxdU_rav, + f"Average dxdU_{d}_{i}": dxdU_av, + f"Surface dxdU_{d}_{i}": dxdU_surf, + f"X-averaged surface dxdU_{d}_{i}": dxdU_surf_av, + } + ) + return variables + + def _get_standard_differential_stoichiometry_variables(self, dxdU): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + dxdU_surf = pybamm.surf(dxdU) + dxdU_surf_av = pybamm.x_average(dxdU_surf) + dxdU_xav = pybamm.x_average(dxdU) + dxdU_rav = pybamm.r_average(dxdU) + dxdU_av = pybamm.r_average(dxdU_xav) + + variables = { + f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dxdU, + f"X-averaged {domain} {phase_name}particle " + "differential stoichiometry [V-1]": dxdU_xav, + f"R-averaged {domain} {phase_name}particle " + "differential stoichiometry [V-1]": dxdU_rav, + f"Average {domain} {phase_name}particle differential " + "stoichiometry [V-1]": dxdU_av, + f"{Domain} {phase_name}particle surface differential " + "stoichiometry [V-1]": dxdU_surf, + f"X-averaged {domain} {phase_name}particle " + "surface differential stoichiometry [V-1]": dxdU_surf_av, + } + + return variables + + def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + # Broadcast and x-average when necessary + if dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + "secondary" + ] != [f"{domain} electrode"]: + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU, [f"{domain} {phase_name}particle"] + ) + + # Surface differential stoichiometry distribution variables + dxdU_surf_xav_distribution = dxdU + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] + ) + + # Differential stoichiometry distribution in all domains. + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + ) + elif dxdU.domain == [f"{domain} {phase_name}particle"] and ( + dxdU.domains["tertiary"] != [f"{domain} electrode"] + ): + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = dxdU + + # Surface differential stoichiometry distribution variables + dxdU_surf_xav_distribution = pybamm.surf(dxdU_xav_distribution) + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] + ) + + # Differential stoichiometry distribution in all domains. + dxdU_distribution = pybamm.TertiaryBroadcast( + dxdU_xav_distribution, [f"{domain} electrode"] + ) + elif dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + "secondary" + ] == [f"{domain} electrode"]: + # Surface differential stoichiometry distribution variables + dxdU_surf_distribution = dxdU + dxdU_surf_xav_distribution = pybamm.x_average(dxdU) + + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] + ) + + # Differential stoichiometry distribution in all domains + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + ) + else: + dxdU_distribution = dxdU + + # x-average the *tertiary* domain. + # NOTE: not yet implemented. Make 0.5 everywhere + dxdU_xav_distribution = pybamm.FullBroadcast( + 0.5, + [f"{domain} {phase_name}particle"], + { + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + + # Surface differential stoichiometry distribution variables + dxdU_surf_distribution = pybamm.surf(dxdU) + dxdU_surf_xav_distribution = pybamm.x_average(dxdU_surf_distribution) + + dxdU_rav_distribution = pybamm.r_average(dxdU_distribution) + dxdU_av_distribution = pybamm.x_average(dxdU_rav_distribution) + + variables = { + f"{Domain} {phase_name}particle differential stoichiometry distribution " + "[V-1]": dxdU_distribution, + f"X-averaged {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_xav_distribution, + f"R-averaged {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_rav_distribution, + f"Average {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_av_distribution, + f"{Domain} {phase_name}particle surface differential stoichiometry" + " distribution [V-1]": dxdU_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface differential " + "stoichiometry distribution [V-1]": dxdU_surf_xav_distribution, + } + return variables diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py index f6a0e09b7d..feffbdd380 100644 --- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py +++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py @@ -50,12 +50,13 @@ def _get_mechanical_results(self, variables): c_0 = domain_param.c_0 E0 = domain_param.E nu = domain_param.nu + L0 = domain_param.L sto_init = pybamm.r_average(domain_param.prim.c_init / domain_param.prim.c_max) v_change = pybamm.x_average( eps_s * domain_param.prim.t_change(sto_rav) ) - pybamm.x_average(eps_s * domain_param.prim.t_change(sto_init)) - electrode_thickness_change = self.param.n_electrodes_parallel * v_change + electrode_thickness_change = self.param.n_electrodes_parallel * v_change * L0 # Ai2019 eq [10] disp_surf = Omega * R0 / 3 * (c_s_rav - c_0) # c0 reference concentration for no deformation diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index 8f555fa9a8..8efd26cd57 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -119,6 +119,9 @@ def _bpx_to_param_dict(bpx: BPX) -> dict: # reference temperature T_ref = pybamm_dict["Reference temperature [K]"] + def arrhenius(Ea, T): + return exp(Ea / constants.R * (1 / T_ref - 1 / T)) + # lumped parameters for name in [ "Specific heat capacity [J.K-1.kg-1]", @@ -258,10 +261,12 @@ def _positive_electrode_entropic_change(sto, c_s_max): "Maximum concentration in " + negative_electrode.pre_name.lower() + "[mol.m-3]" ] k_n_norm = pybamm_dict[ - negative_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" + negative_electrode.pre_name + + "reaction rate constant [mol.m-2.s-1]" ] - E_a_n = pybamm_dict.get( - negative_electrode.pre_name + "reaction rate activation energy [J.mol-1]", 0.0 + Ea_k_n = pybamm_dict.get( + negative_electrode.pre_name + + "reaction rate constant activation energy [J.mol-1]", 0.0 ) # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) @@ -270,10 +275,9 @@ def _positive_electrode_entropic_change(sto, c_s_max): def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): k_ref = k_n # (A/m2)(m3/mol)**1.5 - includes ref concentrations - arrhenius = exp(E_a_n / constants.R * (1 / T_ref - 1 / T)) return ( k_ref - * arrhenius + * arrhenius(Ea_k_n, T) * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 @@ -288,10 +292,12 @@ def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): "Maximum concentration in " + positive_electrode.pre_name.lower() + "[mol.m-3]" ] k_p_norm = pybamm_dict[ - positive_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" + positive_electrode.pre_name + + "reaction rate constant [mol.m-2.s-1]" ] - E_a_p = pybamm_dict.get( - positive_electrode.pre_name + "reaction rate activation energy [J.mol-1]", 0.0 + Ea_k_p = pybamm_dict.get( + positive_electrode.pre_name + + "reaction rate constant activation energy [J.mol-1]", 0.0 ) # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) @@ -300,10 +306,9 @@ def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): k_ref = k_p # (A/m2)(m3/mol)**1.5 - includes ref concentrations - arrhenius = exp(E_a_p / constants.R * (1 / T_ref - 1 / T)) return ( k_ref - * arrhenius + * arrhenius(Ea_k_p, T) * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 @@ -316,7 +321,7 @@ def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): # diffusivity # negative electrode - E_a = pybamm_dict.get( + Ea_D_n = pybamm_dict.get( negative_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) D_n_ref = pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] @@ -324,30 +329,27 @@ def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): if callable(D_n_ref): def _negative_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_n_ref(sto) + return arrhenius(Ea_D_n, T) * D_n_ref(sto) elif isinstance(D_n_ref, tuple): def _negative_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) name, (x, y) = D_n_ref - return arrhenius * pybamm.Interpolant( + return arrhenius(Ea_D_n, T) * pybamm.Interpolant( x, y, sto, name=name, interpolator="linear" ) else: def _negative_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_n_ref + return arrhenius(Ea_D_n, T) * D_n_ref pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( _negative_electrode_diffusivity ) # positive electrode - E_a = pybamm_dict.get( + Ea_D_p = pybamm_dict.get( positive_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) D_p_ref = pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] @@ -355,30 +357,27 @@ def _negative_electrode_diffusivity(sto, T): if callable(D_p_ref): def _positive_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_p_ref(sto) + return arrhenius(Ea_D_p, T) * D_p_ref(sto) elif isinstance(D_p_ref, tuple): def _positive_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) name, (x, y) = D_p_ref - return arrhenius * pybamm.Interpolant( + return arrhenius(Ea_D_p, T) * pybamm.Interpolant( x, y, sto, name=name, interpolator="linear" ) else: def _positive_electrode_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_p_ref + return arrhenius(Ea_D_p, T) * D_p_ref pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( _positive_electrode_diffusivity ) # electrolyte - E_a = pybamm_dict.get( + Ea_D_e = pybamm_dict.get( electrolyte.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) D_e_ref = pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] @@ -386,54 +385,48 @@ def _positive_electrode_diffusivity(sto, T): if callable(D_e_ref): def _electrolyte_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_e_ref(sto) + return arrhenius(Ea_D_e, T) * D_e_ref(sto) elif isinstance(D_e_ref, tuple): def _electrolyte_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) name, (x, y) = D_e_ref - return arrhenius * pybamm.Interpolant( + return arrhenius(Ea_D_e, T) * pybamm.Interpolant( x, y, sto, name=name, interpolator="linear" ) else: def _electrolyte_diffusivity(sto, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * D_e_ref + return arrhenius(Ea_D_e, T) * D_e_ref pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] = _copy_func( _electrolyte_diffusivity ) # conductivity - E_a = pybamm_dict.get( + Ea_sigma_e = pybamm_dict.get( electrolyte.pre_name + "conductivity activation energy [J.mol-1]", 0.0 ) - C_e_ref = pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] + sigma_e_ref = pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] - if callable(C_e_ref): + if callable(sigma_e_ref): def _conductivity(c_e, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * C_e_ref(c_e) + return arrhenius(Ea_sigma_e, T) * sigma_e_ref(c_e) - elif isinstance(C_e_ref, tuple): + elif isinstance(sigma_e_ref, tuple): def _conductivity(c_e, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - name, (x, y) = C_e_ref - return arrhenius * pybamm.Interpolant( + name, (x, y) = sigma_e_ref + return arrhenius(Ea_sigma_e, T) * pybamm.Interpolant( x, y, c_e, name=name, interpolator="linear" ) else: def _conductivity(c_e, T): - arrhenius = exp(E_a / constants.R * (1 / T_ref - 1 / T)) - return arrhenius * C_e_ref + return arrhenius(Ea_sigma_e, T) * sigma_e_ref pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] = _copy_func( _conductivity diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py index e4d574daed..946c47f53b 100644 --- a/pybamm/parameters/electrical_parameters.py +++ b/pybamm/parameters/electrical_parameters.py @@ -30,10 +30,10 @@ def _set_parameters(self): ) self.voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]") self.voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]") - self.opc_soc_0_dimensional = pybamm.Parameter( + self.ocp_soc_0_dimensional = pybamm.Parameter( "Open-circuit voltage at 0% SOC [V]" ) - self.opc_soc_100_dimensional = pybamm.Parameter( + self.ocp_soc_100_dimensional = pybamm.Parameter( "Open-circuit voltage at 100% SOC [V]" ) # Current as a function of time diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 0fe8c58167..7de1054c9e 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -13,18 +13,8 @@ class LithiumIonParameters(BaseParameters): ---------- options : dict, optional - A dictionary of options to be passed to the parameters. The options that - can be set are listed below. - - * "particle shape" : str, optional - Sets the model shape of the electrode particles. This is used to - calculate the surface area to volume ratio. Can be "spherical" - (default). TODO: implement "cylindrical" and "platelet". - * "working electrode": str - Which electrode(s) intercalates and which is counter. If "both" - (default), the model is a standard battery. Otherwise can be "negative" - or "positive" to indicate a half-cell model. - + A dictionary of options to be passed to the parameters, see + :class:`pybamm.BatteryModelOptions`. """ def __init__(self, options=None): @@ -85,8 +75,8 @@ def _set_parameters(self): self.n_cells = self.elec.n_cells self.voltage_low_cut = self.elec.voltage_low_cut self.voltage_high_cut = self.elec.voltage_high_cut - self.opc_soc_0_dimensional = self.elec.opc_soc_0_dimensional - self.opc_soc_100_dimensional = self.elec.opc_soc_100_dimensional + self.ocp_soc_0_dimensional = self.elec.ocp_soc_0_dimensional + self.ocp_soc_100_dimensional = self.elec.ocp_soc_100_dimensional # Domain parameters for domain in self.domain_params.values(): @@ -331,7 +321,7 @@ def _set_parameters(self): f"{Domain} electrode reaction-driven LAM factor [m3.mol-1]" ) - # utilisation parameters + # Utilisation parameters self.u_init = pybamm.Parameter( f"Initial {domain} electrode interface utilisation" ) @@ -387,6 +377,7 @@ def __init__(self, phase, domain_param): self.geo = domain_param.geo.prim elif self.phase == "secondary": self.geo = domain_param.geo.sec + self.options = getattr(self.main_param.options, self.domain) def _set_parameters(self): main = self.main_param @@ -470,6 +461,8 @@ def _set_parameters(self): self.U_init = pybamm.Scalar(0) return + # Spatial variables for parameters that depend on position within the cell + # and/or particle x = pybamm.SpatialVariable( f"x_{domain[0]}", domain=[f"{domain} electrode"], @@ -486,53 +479,59 @@ def _set_parameters(self): coord_sys="spherical polar", ) - # Macroscale geometry + # Microscale geometry # Note: the surface area to volume ratio is defined later with the function # parameters. The particle size as a function of through-cell position is # already defined in geometric_parameters.py self.R = self.geo.R self.R_typ = self.geo.R_typ - - # Particle properties - self.c_max = pybamm.Parameter( - f"{pref}Maximum concentration in {domain} electrode [mol.m-3]" - ) - # Particle-size distribution parameters self.R_min = self.geo.R_min self.R_max = self.geo.R_max self.f_a_dist = self.geo.f_a_dist + # Particle properties self.epsilon_s = pybamm.FunctionParameter( f"{pref}{Domain} electrode active material volume fraction", {"Through-cell distance (x) [m]": x}, ) - self.c_init = pybamm.FunctionParameter( - f"{pref}Initial concentration in {domain} electrode [mol.m-3]", - { - "Radial distance (r) [m]": r, - "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( - x, f"{domain} {phase_name}particle" - ), - }, + self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s) + self.c_max = pybamm.Parameter( + f"{pref}Maximum concentration in {domain} electrode [mol.m-3]" ) + if self.options["open-circuit potential"] == "MSMR": + self.U_init = pybamm.Parameter( + f"{pref}Initial voltage in {domain} electrode [V]", + ) + self.c_init = self.x(self.U_init) * self.c_max + else: + self.c_init = pybamm.FunctionParameter( + f"{pref}Initial concentration in {domain} electrode [mol.m-3]", + { + "Radial distance (r) [m]": r, + "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( + x, f"{domain} {phase_name}particle" + ), + }, + ) self.c_init_av = pybamm.xyz_average(pybamm.r_average(self.c_init)) self.sto_init_av = self.c_init_av / self.c_max eps_c_init_av = pybamm.xyz_average( self.epsilon_s * pybamm.r_average(self.c_init) ) - self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc - self.Q_Li_init = self.n_Li_init * main.F / 3600 - self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s) + if self.options["open-circuit potential"] != "MSMR": + self.U_init = self.U(self.sto_init_av, main.T_init) + + # Electrode loading and capacity self.elec_loading = ( self.epsilon_s_av * self.domain_param.L * self.c_max * main.F / 3600 ) + self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc + self.Q_Li_init = self.n_Li_init * main.F / 3600 self.Q_init = self.elec_loading * main.A_cc - self.U_init = self.U(self.sto_init_av, main.T_init) - - if main.options["particle shape"] == "spherical": + if self.options["particle shape"] == "spherical": self.a_typ = 3 * pybamm.xyz_average(self.epsilon_s) / self.R_typ def D(self, c_s, T, lithiation=None): @@ -630,6 +629,114 @@ def dUdT(self, sto): inputs, ) + def X_j(self, index): + "Available host sites indexed by reaction j" + domain = self.domain + d = domain[0] + Xj = pybamm.Parameter(f"X_{d}_{index}") + return Xj + + def U0_j(self, index): + "Equilibrium potential indexed by reaction j" + domain = self.domain + d = domain[0] + U0j = pybamm.Parameter(f"U0_{d}_{index}") + return U0j + + def w_j(self, index): + "Order parameter indexed by reaction j" + domain = self.domain + d = domain[0] + wj = pybamm.Parameter(f"w_{d}_{index}") + return wj + + def alpha_bv_j(self, index): + "Dimensional Butler-Volmer exchange-current density indexed by reaction j" + domain = self.domain + d = domain[0] + alpha_bv_j = pybamm.Parameter(f"a_{d}_{index}") + return alpha_bv_j + + def x_j(self, U, index): + "Fractional occupancy of site j as a function of potential" + T = self.main_param.T_ref + f = self.main_param.F / (self.main_param.R * T) + U0j = self.U0_j(index) + wj = self.w_j(index) + Xj = self.X_j(index) + # Equation 5, Baker et al 2018 + xj = Xj / (1 + pybamm.exp(f * (U - U0j) / wj)) + return xj + + def dxdU_j(self, U, index): + "Derivative of fractional occupancy of site j as a function of potential [V-1]" + T = self.main_param.T_ref + f = self.main_param.F / (self.main_param.R * T) + U0j = self.U0_j(index) + wj = self.w_j(index) + Xj = self.X_j(index) + e = pybamm.exp(f * (U - U0j) / wj) + # Equation 25, Baker et al 2018 + dxjdU = -(f / wj) * (Xj * e) / (1 + e) ** 2 + return dxjdU + + def j0_j(self, c_e, U, T, index): + "Exchange-current density index by reaction j [A.m-2]" + domain = self.domain + d = domain[0] + + tol = pybamm.settings.tolerances["j0__c_e"] + c_e = pybamm.maximum(c_e, tol) + c_e_ref = self.main_param.c_e_init + xj = self.x_j(U, index) + # xj = pybamm.maximum(pybamm.minimum(xj, (1 - tol)), tol) + + f = self.main_param.F / (self.main_param.R * T) + wj = self.w_j(index) + self.X_j(index) + aj = self.alpha_bv_j(index) + j0_ref_j = pybamm.FunctionParameter( + f"j0_ref_{d}_{index}", {"Temperature [K]": T} + ) + + # Equation 16, Baker et al 2018. The original formulation would be implemented + # as: + # j0_j = ( + # j0_ref_j + # * xj ** (wj * aj) + # * (Xj - xj) ** (wj * (1 - aj)) + # * (c_e / c_e_ref) ** (1 - aj) + # ) + # However, we reformulate in terms of potential to avoid singularity as x_j + # approaches X_j + j0_j = ( + j0_ref_j + * xj**wj + * pybamm.exp(f * (1 - aj) * (U - self.U0_j(index))) + * (c_e / c_e_ref) ** (1 - aj) + ) + return j0_j + + def x(self, U): + "Stoichiometry as a function of potential (for use with MSMR models)" + N = int(self.options["number of MSMR reactions"]) + # Equation 6, Baker et al 2018 + x = 0 + for i in range(N): + x += self.x_j(U, i) + return x + + def dxdU(self, U): + """ + Differential stoichiometry as a function of potential (for use with MSMR models) + """ + N = int(self.options["number of MSMR reactions"]) + # Equation 25, Baker et al 2018 + dxdU = 0 + for i in range(N): + dxdU += self.dxdU_j(U, i) + return dxdU + def t_change(self, sto): """ Volume change for the electrode; sto should be R-averaged diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index c78cb70c91..136d9737aa 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -259,14 +259,15 @@ def set_initial_stoichiometries( param=None, known_value="cyclable lithium capacity", inplace=True, + options=None, ): """ Set the initial stoichiometry of each electrode, based on the initial SOC or voltage """ - param = param or pybamm.LithiumIonParameters() + param = param or pybamm.LithiumIonParameters(options) x, y = pybamm.lithium_ion.get_initial_stoichiometries( - initial_value, self, param=param, known_value=known_value + initial_value, self, param=param, known_value=known_value, options=options ) if inplace: parameter_values = self @@ -282,6 +283,34 @@ def set_initial_stoichiometries( ) return parameter_values + def set_initial_ocps( + self, + initial_value, + param=None, + known_value="cyclable lithium capacity", + inplace=True, + options=None, + ): + """ + Set the initial OCP of each electrode, based on the initial + SOC or voltage + """ + param = param or pybamm.LithiumIonParameters(options) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + initial_value, self, param=param, known_value=known_value, options=options + ) + if inplace: + parameter_values = self + else: + parameter_values = self.copy() + parameter_values.update( + { + "Initial voltage in negative electrode [V]": Un, + "Initial voltage in positive electrode [V]": Up, + } + ) + return parameter_values + def check_parameter_values(self, values): for param in values: if "propotional term" in param: diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 52c1922545..4b65b973aa 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -255,7 +255,7 @@ def set_up_and_parameterise_model_for_experiment(self): parameterised_model = new_parameter_values.process_model( new_model, inplace=False ) - self.experiment_unique_steps_to_model[repr(op)] = parameterised_model + self.experiment_unique_steps_to_model[op.basic_repr()] = parameterised_model # Set up rest model if experiment has start times if self.experiment.initial_start_time: @@ -371,12 +371,19 @@ def set_initial_soc(self, initial_soc): self.op_conds_to_built_models = None self.op_conds_to_built_solvers = None + options = self.model.options param = self.model.param - self.parameter_values = ( - self._unprocessed_parameter_values.set_initial_stoichiometries( - initial_soc, param=param, inplace=False + if options["open-circuit potential"] == "MSMR": + self.parameter_values = self._unprocessed_parameter_values.set_initial_ocps( + initial_soc, param=param, inplace=False, options=options ) - ) + else: + self.parameter_values = ( + self._unprocessed_parameter_values.set_initial_stoichiometries( + initial_soc, param=param, inplace=False, options=options + ) + ) + # Save solved initial SOC in case we need to re-build the model self._built_initial_soc = initial_soc @@ -641,6 +648,21 @@ def solve( starting_solution.all_first_states.copy() ) + # set simulation initial_start_time + if starting_solution is None: + initial_start_time = self.experiment.initial_start_time + else: + initial_start_time = starting_solution.initial_start_time + + if ( + initial_start_time is None + and self.experiment.initial_start_time is not None + ): + raise ValueError( + "When using experiments with `start_time`, the starting_solution " + "must have a `start_time` too." + ) + cycle_offset = len(starting_solution_cycles) all_cycle_solutions = starting_solution_cycles all_summary_variables = starting_solution_summary_variables @@ -653,6 +675,49 @@ def solve( idx = 0 num_cycles = len(self.experiment.cycle_lengths) feasible = True # simulation will stop if experiment is infeasible + + # Add initial padding rest if current time is earlier than first start time + # This could be the case when using a starting solution + if starting_solution is not None: + op_conds = self.experiment.operating_conditions_steps[0] + if op_conds.start_time is not None: + rest_time = ( + op_conds.start_time + - ( + initial_start_time + + timedelta(seconds=float(current_solution.t[-1])) + ) + ).total_seconds() + if rest_time > pybamm.settings.step_start_offset: + # logs["step operating conditions"] = "Initial rest for padding" + # callbacks.on_step_start(logs) + + kwargs["inputs"] = { + **user_inputs, + "Ambient temperature [K]": ( + op_conds.temperature or self._original_temperature + ), + "start time": current_solution.t[-1], + } + steps = current_solution.cycles[-1].steps + step_solution = current_solution.cycles[-1].steps[-1] + + step_solution_with_rest = self.run_padding_rest( + kwargs, rest_time, step_solution + ) + steps[-1] = step_solution + step_solution_with_rest + + cycle_solution, _, _ = pybamm.make_cycle_solution( + steps, esoh_solver=esoh_solver, save_this_cycle=True + ) + old_cycles = current_solution.cycles.copy() + old_cycles[-1] = cycle_solution + current_solution += step_solution_with_rest + current_solution.cycles = old_cycles + + # Update _solution + self._solution = current_solution + for cycle_num, cycle_length in enumerate( # tqdm is the progress bar. tqdm.tqdm( @@ -676,6 +741,8 @@ def solve( save_this_cycle = ( # always save cycle 1 cycle_num == 1 + # always save last cycle + or cycle_num == num_cycles # None: save all cycles or save_at_cycles is None # list: save all cycles in the list @@ -703,7 +770,7 @@ def solve( ( op_conds.end_time - ( - self.experiment.initial_start_time + initial_start_time + timedelta(seconds=float(start_time)) ) ).total_seconds(), @@ -711,8 +778,8 @@ def solve( else: dt = op_conds.duration op_conds_str = str(op_conds) - model = self.op_conds_to_built_models[repr(op_conds)] - solver = self.op_conds_to_built_solvers[repr(op_conds)] + model = self.op_conds_to_built_models[op_conds.basic_repr()] + solver = self.op_conds_to_built_solvers[op_conds.basic_repr()] logs["step number"] = (step_num, cycle_length) logs["step operating conditions"] = op_conds_str @@ -758,41 +825,25 @@ def solve( rest_time = ( op_conds.next_start_time - ( - self.experiment.initial_start_time + initial_start_time + timedelta(seconds=float(step_solution.t[-1])) ) ).total_seconds() if rest_time > pybamm.settings.step_start_offset: - start_time = step_solution.t[-1] - # Let me know if you have a better name - op_conds_str = "Rest for padding" - model = self.op_conds_to_built_models[op_conds_str] - solver = self.op_conds_to_built_solvers[op_conds_str] - logs["step number"] = (step_num, cycle_length) - logs["step operating conditions"] = op_conds_str + logs["step operating conditions"] = "Rest for padding" callbacks.on_step_start(logs) - ambient_temp = ( - op_conds.temperature or self._original_temperature - ) kwargs["inputs"] = { **user_inputs, - "Ambient temperature [K]": ambient_temp, - "start time": start_time, + "Ambient temperature [K]": ( + op_conds.temperature or self._original_temperature + ), + "start time": step_solution.t[-1], } - # Make sure we take at least 2 timesteps - # The period is hardcoded to 10 minutes, the user can - # always override it by adding a rest step - npts = max(int(round(rest_time / 600)) + 1, 2) - - step_solution_with_rest = solver.step( - step_solution, - model, - rest_time, - npts=npts, - save=False, - **kwargs, + + step_solution_with_rest = self.run_padding_rest( + kwargs, rest_time, step_solution ) step_solution += step_solution_with_rest @@ -896,8 +947,30 @@ def solve( callbacks.on_experiment_end(logs) + # record initial_start_time of the solution + self.solution.initial_start_time = initial_start_time + return self.solution + def run_padding_rest(self, kwargs, rest_time, step_solution): + model = self.op_conds_to_built_models["Rest for padding"] + solver = self.op_conds_to_built_solvers["Rest for padding"] + + # Make sure we take at least 2 timesteps. The period is hardcoded to 10 + # minutes,the user can always override it by adding a rest step + npts = max(int(round(rest_time / 600)) + 1, 2) + + step_solution_with_rest = solver.step( + step_solution, + model, + rest_time, + npts=npts, + save=False, + **kwargs, + ) + + return step_solution_with_rest + def step( self, dt, solver=None, npts=2, save=True, starting_solution=None, **kwargs ): @@ -948,7 +1021,7 @@ def _get_esoh_solver(self, calc_esoh): return None return pybamm.lithium_ion.ElectrodeSOHSolver( - self.parameter_values, self.model.param + self.parameter_values, self.model.param, options=self.model.options ) def plot(self, output_variables=None, **kwargs): diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index 7740006310..a92a9309d4 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -748,13 +748,6 @@ def solve( self._set_up_model_inputs(model, inputs) for inputs in inputs_list ] - # Cannot use multiprocessing with model in "jax" format - if (len(inputs_list) > 1) and model.convert_to_format == "jax": - raise pybamm.SolverError( - "Cannot solve list of inputs with multiprocessing " - 'when model in format "jax".' - ) - # Check that calculate_sensitivites have not been updated calculate_sensitivities_list.sort() if not hasattr(model, "calculate_sensitivities"): @@ -864,17 +857,25 @@ def solve( ) new_solutions = [new_solution] else: - with mp.Pool(processes=nproc) as p: - new_solutions = p.starmap( - self._integrate, - zip( - [model] * ninputs, - [t_eval[start_index:end_index]] * ninputs, - model_inputs_list, - ), + if model.convert_to_format == "jax": + # Jax can parallelize over the inputs efficiently + new_solutions = self._integrate( + model, + t_eval[start_index:end_index], + model_inputs_list, ) - p.close() - p.join() + else: + with mp.Pool(processes=nproc) as p: + new_solutions = p.starmap( + self._integrate, + zip( + [model] * ninputs, + [t_eval[start_index:end_index]] * ninputs, + model_inputs_list, + ), + ) + p.close() + p.join() # Setting the solve time for each segment. # pybamm.Solution.__add__ assumes attribute solve_time. solve_time = timer.time() diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py index 36d837bee1..8e7b1b5cc5 100644 --- a/pybamm/solvers/jax_solver.py +++ b/pybamm/solvers/jax_solver.py @@ -2,6 +2,7 @@ # Solver class using Scipy's adaptive time stepper # import numpy as onp +import asyncio import pybamm @@ -164,7 +165,7 @@ def solve_model_rk45(inputs): inputs, rtol=self.rtol, atol=self.atol, - **self.extra_options + **self.extra_options, ) return jnp.transpose(y) @@ -177,7 +178,7 @@ def solve_model_bdf(inputs): rtol=self.rtol, atol=self.atol, mass=mass, - **self.extra_options + **self.extra_options, ) return jnp.transpose(y) @@ -186,7 +187,7 @@ def solve_model_bdf(inputs): else: return jax.jit(solve_model_bdf) - def _integrate(self, model, t_eval, inputs_dict=None): + def _integrate(self, model, t_eval, inputs=None): """ Solve a model defined by dydt with initial conditions y0. @@ -196,7 +197,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): The model whose solution to calculate. t_eval : :class:`numpy.array`, size (k,) The times at which to compute the solution - inputs_dict : dict, optional + inputs : dict, list[dict], optional Any input parameters to pass to the model when solving Returns @@ -206,11 +207,74 @@ def _integrate(self, model, t_eval, inputs_dict=None): various diagnostic messages. """ + if isinstance(inputs, dict): + inputs = [inputs] timer = pybamm.Timer() if model not in self._cached_solves: self._cached_solves[model] = self.create_solve(model, t_eval) - y = self._cached_solves[model](inputs_dict).block_until_ready() + y = [] + platform = jax.lib.xla_bridge.get_backend().platform.casefold() + if platform.startswith("cpu"): + # cpu execution runs faster when multithreaded + async def solve_model_for_inputs(): + async def solve_model_async(inputs_v): + return self._cached_solves[model](inputs_v) + + coro = [] + for inputs_v in inputs: + coro.append(asyncio.create_task(solve_model_async(inputs_v))) + return await asyncio.gather(*coro) + + y = asyncio.run(solve_model_for_inputs()) + elif platform.startswith("gpu") or platform.startswith("tpu"): + # gpu execution runs faster when parallelised with vmap + # (see also comment below regarding single-program multiple-data + # execution (SPMD) using pmap on multiple XLAs) + + # convert inputs (array of dict) to a dict of arrays for vmap + inputs_v = { + key: jnp.array([dic[key] for dic in inputs]) for key in inputs[0] + } + y.extend(jax.vmap(self._cached_solves[model])(inputs_v)) + else: + # Unknown platform, use serial execution as fallback + print( + f'Unknown platform requested: "{platform}", ' + "falling back to serial execution" + ) + for inputs_v in inputs: + y.append(self._cached_solves[model](inputs_v)) + + # This code block implements single-program multiple-data execution + # using pmap across multiple XLAs. It is currently commented out + # because it produces bus errors for even moderate-sized models. + # It is suspected that this is due to either a bug in JAX, insufficient + # sparse matrix support in JAX resulting in high memory usage, or a bug + # in the BDF solver. + # + # This issue on guthub appears related: + # https://github.com/google/jax/discussions/13930 + # + # # Split input list based on the number of available xla devices + # device_count = jax.local_device_count() + # inputs_listoflists = [inputs[x:x + device_count] + # for x in range(0, len(inputs), device_count)] + # if len(inputs_listoflists) > 1: + # print(f"{len(inputs)} parameter sets were provided, " + # f"but only {device_count} XLA devices are available") + # print(f"Parameter sets split into {len(inputs_listoflists)} " + # "lists for parallel processing") + # y = [] + # for k, inputs_list in enumerate(inputs_listoflists): + # if len(inputs_listoflists) > 1: + # print(f" Solving list {k+1} of {len(inputs_listoflists)} " + # f"({len(inputs_list)} parameter sets)") + # # convert inputs to a dict of arrays for pmap + # inputs_v = {key: jnp.array([dic[key] for dic in inputs_list]) + # for key in inputs_list[0]} + # y.extend(jax.pmap(self._cached_solves[model])(inputs_v)) + integration_time = timer.time() # convert to a normal numpy array @@ -219,8 +283,22 @@ def _integrate(self, model, t_eval, inputs_dict=None): termination = "final time" t_event = None y_event = onp.array(None) - sol = pybamm.Solution( - t_eval, y, model, inputs_dict, t_event, y_event, termination - ) - sol.integration_time = integration_time - return sol + + # Extract solutions from y with their associated input dicts + solutions = [] + for k, inputs_dict in enumerate(inputs): + sol = pybamm.Solution( + t_eval, + jnp.reshape(y[k,], y.shape[1:]), + model, + inputs_dict, + t_event, + y_event, + termination, + ) + sol.integration_time = integration_time + solutions.append(sol) + + if len(solutions) == 1: + return solutions[0] + return solutions diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 65c3e0b5a7..4c9ccb993d 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -131,6 +131,9 @@ def __init__( # Initialize empty summary variables self._summary_variables = None + # Initialise initial start time + self.initial_start_time = None + # Solution now uses CasADi pybamm.citations.register("Andersson2019") @@ -434,6 +437,15 @@ def cycles(self, cycles): def summary_variables(self): return self._summary_variables + @property + def initial_start_time(self): + return self._initial_start_time + + @initial_start_time.setter + def initial_start_time(self, value): + """Updates the initial start time of the experiment""" + self._initial_start_time = value + def set_summary_variables(self, all_summary_variables): summary_variables = {var: [] for var in all_summary_variables[0]} for sum_vars in all_summary_variables: diff --git a/pybamm/step/_steps_util.py b/pybamm/step/_steps_util.py index 879461b73c..e524bc6064 100644 --- a/pybamm/step/_steps_util.py +++ b/pybamm/step/_steps_util.py @@ -71,22 +71,25 @@ def __init__( ): self.type = typ - # Record all the args for repr - self.args = f"{typ}, {value}" + # Record all the args for repr and hash + self.repr_args = f"{typ}, {value}" + self.hash_args = f"{typ}, {value}" if duration: - self.args += f", duration={duration}" + self.repr_args += f", duration={duration}" if termination: - self.args += f", termination={termination}" + self.repr_args += f", termination={termination}" + self.hash_args += f", termination={termination}" if period: - self.args += f", period={period}" + self.repr_args += f", period={period}" if temperature: - self.args += f", temperature={temperature}" + self.repr_args += f", temperature={temperature}" + self.hash_args += f", temperature={temperature}" if tags: - self.args += f", tags={tags}" + self.repr_args += f", tags={tags}" if start_time: - self.args += f", start_time={start_time}" + self.repr_args += f", start_time={start_time}" if description: - self.args += f", description={description}" + self.repr_args += f", description={description}" # Check if drive cycle self.is_drive_cycle = isinstance(value, np.ndarray) @@ -158,7 +161,15 @@ def __str__(self): return repr(self) def __repr__(self): - return f"_Step({self.args})" + return f"_Step({self.repr_args})" + + def basic_repr(self): + """ + Return a basic representation of the step, only with type, value, termination + and temperature, which are the variables involved in processing the model. Also + used for hashing. + """ + return f"_Step({self.hash_args})" def to_dict(self): """ @@ -184,13 +195,11 @@ def to_dict(self): def __eq__(self, other): return ( isinstance(other, _Step) - and self.__repr__() == other.__repr__() - and self.next_start_time == other.next_start_time - and self.end_time == other.end_time + and self.hash_args == other.hash_args ) def __hash__(self): - return hash(repr(self)) + return hash(self.basic_repr()) @property def unit(self): diff --git a/pybamm/util.py b/pybamm/util.py index 5f84f37e0a..b0fa9c822e 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -23,8 +23,8 @@ import pybamm # versions of jax and jaxlib compatible with PyBaMM -JAX_VERSION = "0.4.8" -JAXLIB_VERSION = "0.4.7" +JAX_VERSION = "0.4" +JAXLIB_VERSION = "0.4" def root_dir(): @@ -271,10 +271,9 @@ def have_jax(): def is_jax_compatible(): """Check if the available version of jax and jaxlib are compatible with PyBaMM""" - return ( - pkg_resources.get_distribution("jax").version == JAX_VERSION - and pkg_resources.get_distribution("jaxlib").version == JAXLIB_VERSION - ) + return pkg_resources.get_distribution("jax").version.startswith( + JAX_VERSION + ) and pkg_resources.get_distribution("jaxlib").version.startswith(JAXLIB_VERSION) def is_constant_and_can_evaluate(symbol): @@ -341,7 +340,7 @@ def install_jax(arguments=None): # pragma: no cover "-m", "pip", "install", - f"jax=={JAX_VERSION}", - f"jaxlib=={JAXLIB_VERSION}", + f"jax>={JAX_VERSION}", + f"jaxlib>={JAXLIB_VERSION}", ] ) diff --git a/scripts/update_version.py b/scripts/update_version.py index 4a5f60d8d8..fb9b15dd31 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -5,10 +5,10 @@ import json import os import re -from datetime import date, datetime - +from datetime import date from dateutil.relativedelta import relativedelta + import pybamm @@ -16,12 +16,10 @@ def update_version(): """ Opens file and updates the version number """ - current_year = datetime.now().strftime("%y") - current_month = datetime.now().month - - release_version = f"{current_year}.{current_month}" + release_version = os.getenv("VERSION")[1:] last_day_of_month = date.today() + relativedelta(day=31) + # pybamm/version.py with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file: output = file.read() @@ -41,23 +39,24 @@ def update_version(): file.write(replace_version) # docs/source/_static/versions.json for readthedocs build - with open( - os.path.join(pybamm.root_dir(), "docs", "source", "_static", "versions.json"), - "r+", - ) as file: - output = file.read() - json_data = json.loads(output) - json_data.insert( - 2, - { - "name": f"v{release_version}", - "version": f"{release_version}", - "url": f"https://docs.pybamm.org/en/v{release_version}/", - }, - ) - file.truncate(0) - file.seek(0) - file.write(json.dumps(json_data, indent=4)) + if "rc" not in release_version: + with open( + os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"), + "r+", + ) as file: + output = file.read() + json_data = json.loads(output) + json_data.insert( + 2, + { + "name": f"v{release_version}", + "version": f"{release_version}", + "url": f"https://docs.pybamm.org/en/v{release_version}/", + }, + ) + file.truncate(0) + file.seek(0) + file.write(json.dumps(json_data, indent=4)) # vcpkg.json with open(os.path.join(pybamm.root_dir(), "vcpkg.json"), "r+") as file: @@ -90,40 +89,14 @@ def update_version(): with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file: output_list = file.readlines() output_list[0] = changelog_line1 - output_list.insert(2, changelog_line2) + if "rc0" in release_version: + output_list.insert(2, changelog_line2) + else: + output_list[2] = changelog_line2 file.truncate(0) file.seek(0) file.writelines(output_list) -def get_changelog(): - """ - Opens CHANGELOG.md and overrides the changelog with the latest version. - Used in GitHub workflow to create the changelog for the GitHub release. - """ - # This month - now = datetime.now() - current_year = now.strftime("%y") - current_month = now.month - - # Previous month - previous_date = datetime.now() + relativedelta(months=-1) - previous_year = previous_date.strftime("%y") - previous_month = previous_date.month - - current_version = re.escape(f"# [v{current_year}.{current_month}]") - previous_version = re.escape(f"# [v{previous_year}.{previous_month}]") - - # Open CHANGELOG.md and keep the relevant lines - with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file: - output = file.read() - re_changelog = f"{current_version}.*?(##.*)(?={previous_version})" - release_changelog = re.findall(re_changelog, output, re.DOTALL)[0] - print(release_changelog) - file.truncate(0) - file.seek(0) - file.write(release_changelog) - - if __name__ == "__main__": update_version() diff --git a/setup.py b/setup.py index dfdd455a16..df55a24325 100644 --- a/setup.py +++ b/setup.py @@ -300,6 +300,7 @@ def compile_KLU(): "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values", # noqa: E501 "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values", # noqa: E501 "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values", # noqa: E501 + "MSMR_Example = pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values", # noqa: E501 ], }, ) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 01fb8b8d4d..6c787cea0b 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -308,3 +308,15 @@ def test_composite_graphite_silicon_sei(self): {f"Primary: {name}": (1 - x) * 0.75, f"Secondary: {name}": x * 0.75} ) self.run_basic_processing_test(options, parameter_values=parameter_values) + + def test_basic_processing_msmr(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + "number of MSMR reactions": ("6", "4"), + } + parameter_values = pybamm.ParameterValues("MSMR_Example") + model = self.model(options) + modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) + modeltest.test_all(skip_output_tests=True) diff --git a/tests/testcase.py b/tests/testcase.py index 4132954d31..f2daa7ba9a 100644 --- a/tests/testcase.py +++ b/tests/testcase.py @@ -16,7 +16,7 @@ def FixRandomSeed(method): explicitely reinstate the random seed within their method bodies as desired, e.g. by calling np.random.seed(None) to restore normal behaviour. - Generatig a random seed from the method name allows particularly awkward + Generating a random seed from the method name allows particularly awkward sequences to be altered by changing the method name, such as by adding a trailing underscore, or other hash modifier, if required. """ diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py index bfa5820382..5fde193af3 100644 --- a/tests/unit/test_citations.py +++ b/tests/unit/test_citations.py @@ -338,6 +338,18 @@ def test_sripad_2020(self): self.assertIn("Sripad2020", citations._papers_to_cite) self.assertIn("Sripad2020", citations._citation_tags.keys()) + def test_msmr(self): + citations = pybamm.citations + + citations._reset() + self.assertNotIn("Baker2018", citations._papers_to_cite) + self.assertNotIn("Verbrugge2017", citations._papers_to_cite) + pybamm.particle.MSMRDiffusion(None, "negative", None, None, None) + self.assertIn("Baker2018", citations._papers_to_cite) + self.assertIn("Baker2018", citations._citation_tags.keys()) + self.assertIn("Verbrugge2017", citations._papers_to_cite) + self.assertIn("Verbrugge2017", citations._citation_tags.keys()) + def test_parameter_citations(self): citations = pybamm.citations @@ -379,6 +391,13 @@ def test_parameter_citations(self): self.assertIn("ORegan2022", citations._papers_to_cite) self.assertIn("ORegan2022", citations._citation_tags.keys()) + citations._reset() + pybamm.ParameterValues("MSMR_Example") + self.assertIn("Baker2018", citations._papers_to_cite) + self.assertIn("Baker2018", citations._citation_tags.keys()) + self.assertIn("Verbrugge2017", citations._papers_to_cite) + self.assertIn("Verbrugge2017", citations._citation_tags.keys()) + def test_solver_citations(self): # Test that solving each solver adds the right citations citations = pybamm.citations diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 460d82c8c6..6688fae5b1 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -38,8 +38,12 @@ def test_set_up(self): [3600, 3 / Crate * 3600, 24 * 3600, 24 * 3600], ) - model_I = sim.experiment_unique_steps_to_model[repr(op_conds[1])] # CC charge - model_V = sim.experiment_unique_steps_to_model[repr(op_conds[2])] # CV hold + model_I = sim.experiment_unique_steps_to_model[ + op_conds[1].basic_repr() + ] # CC charge + model_V = sim.experiment_unique_steps_to_model[ + op_conds[2].basic_repr() + ] # CV hold self.assertIn( "Current cut-off [A] [experiment]", [event.name for event in model_V.events], @@ -382,9 +386,9 @@ def test_save_at_cycles(self): solver=pybamm.CasadiSolver("fast with events"), save_at_cycles=[3, 4, 5, 9] ) # Note offset by 1 (0th cycle is cycle 1) - for cycle_num in [1, 5, 6, 7, 9]: + for cycle_num in [1, 5, 6, 7]: self.assertIsNone(sol.cycles[cycle_num]) - for cycle_num in [0, 2, 3, 4, 8]: + for cycle_num in [0, 2, 3, 4, 8, 9]: # first & last cycle always saved self.assertIsNotNone(sol.cycles[cycle_num]) # Summary variables are not None self.assertIsNotNone(sol.summary_variables["Capacity [A.h]"]) @@ -605,7 +609,7 @@ def test_padding_rest_model(self): pybamm.lithium_ion.SPM, ) - def test_run_time_stamped_experiment(self): + def test_run_start_time_experiment(self): model = pybamm.lithium_ion.SPM() # Test experiment is cut short if next_start_time is early @@ -640,6 +644,127 @@ def test_run_time_stamped_experiment(self): sol = sim.solve(calc_esoh=False) self.assertEqual(sol["Time [s]"].entries[-1], 10800) + def test_starting_solution(self): + model = pybamm.lithium_ion.SPM() + + experiment = pybamm.Experiment( + [ + pybamm.step.string("Discharge at C/2 for 10 minutes"), + pybamm.step.string("Rest for 5 minutes"), + pybamm.step.string("Rest for 5 minutes"), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve(save_at_cycles=[1]) + + # test that the last state is correct (i.e. final cycle is saved) + self.assertEqual(solution.last_state.t[-1], 1200) + + experiment = pybamm.Experiment( + [ + pybamm.step.string("Discharge at C/2 for 20 minutes"), + pybamm.step.string("Rest for 20 minutes"), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + new_solution = sim.solve(calc_esoh=False, starting_solution=solution) + + # test that the final time is correct (i.e. starting solution correctly set) + self.assertEqual(new_solution["Time [s]"].entries[-1], 3600) + + def test_experiment_start_time_starting_solution(self): + model = pybamm.lithium_ion.SPM() + + # Test error raised if starting_solution does not have start_time + experiment = pybamm.Experiment( + [pybamm.step.string("Discharge at C/2 for 10 minutes")] + ) + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 0, 0), + ) + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + with self.assertRaisesRegex(ValueError, "experiments with `start_time`"): + sim.solve(starting_solution=solution) + + # Test starting_solution works well with start_time + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 8, 0, 0), + ), + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 8, 20, 0), + ), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 0, 0), + ), + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 20, 0), + ), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + new_solution = sim.solve(starting_solution=solution) + + # test that the final time is correct (i.e. starting solution correctly set) + self.assertEqual(new_solution["Time [s]"].entries[-1], 5400) + + def test_experiment_start_time_identical_steps(self): + # Test that if we have the same step twice, with different start times, + # they get processed only once + model = pybamm.lithium_ion.SPM() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(2023, 1, 1, 8, 0, 0), + ), + pybamm.step.string("Discharge at C/3 for 10 minutes"), + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(2023, 1, 1, 10, 0, 0), + ), + pybamm.step.string("Discharge at C/3 for 10 minutes"), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + sim.solve(calc_esoh=False) + + # Check that there are 4 steps + self.assertEqual(len(experiment.operating_conditions_steps), 4) + + # Check that there are only 2 unique steps + self.assertEqual(len(sim.experiment.unique_steps), 2) + + # Check that there are only 3 built models (unique steps + padding rest) + self.assertEqual(len(sim.op_conds_to_built_models), 3) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py index f9500a6f90..81d1210229 100644 --- a/tests/unit/test_expression_tree/test_broadcasts.py +++ b/tests/unit/test_expression_tree/test_broadcasts.py @@ -332,6 +332,24 @@ def test_to_equation(self): a = pybamm.PrimaryBroadcast(0, "test").to_equation() self.assertEqual(a, 0) + def test_diff(self): + a = pybamm.StateVector(slice(0, 1)) + b = pybamm.PrimaryBroadcast(a, "separator") + y = np.array([5]) + # diff of broadcast is broadcast of diff + d = b.diff(a) + self.assertIsInstance(d, pybamm.PrimaryBroadcast) + self.assertEqual(d.child.evaluate(y=y), 1) + # diff of broadcast w.r.t. itself is 1 + d = b.diff(b) + self.assertIsInstance(d, pybamm.Scalar) + self.assertEqual(d.evaluate(y=y), 1) + # diff of broadcast of a constant is 0 + c = pybamm.PrimaryBroadcast(pybamm.Scalar(4), "separator") + d = c.diff(a) + self.assertIsInstance(d, pybamm.Scalar) + self.assertEqual(d.evaluate(y=y), 0) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 28277af9e2..60eed9d6fb 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -26,14 +26,15 @@ 'electrolyte conductivity': 'default' (possible: ['default', 'full', 'leading order', 'composite', 'integrated']) 'exchange-current density': 'single' (possible: ['single', 'current sigmoid']) 'hydrolysis': 'false' (possible: ['false', 'true']) -'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey']) +'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey', 'MSMR']) 'interface utilisation': 'full' (possible: ['full', 'constant', 'current-driven']) 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible']) 'lithium plating porosity change': 'false' (possible: ['false', 'true']) 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'current-driven', 'stress and reaction-driven']) -'open-circuit potential': 'single' (possible: ['single', 'current sigmoid']) +'number of MSMR reactions': 'none' (possible: ['none']) +'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) -'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile']) +'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR']) 'particle mechanics': 'swelling only' (possible: ['none', 'swelling only', 'swelling and cracking']) 'particle phases': '1' (possible: ['1', '2']) 'particle shape': 'spherical' (possible: ['spherical', 'no particles']) @@ -368,6 +369,35 @@ def test_options(self): with self.assertRaisesRegex(pybamm.OptionError, "multiple particle phases"): pybamm.BaseBatteryModel({"particle phases": "2", "surface form": "false"}) + # msmr + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"open-circuit potential": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"particle": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"intercalation kinetics": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"open-circuit potential": "MSMR", "particle": "MSMR"} + ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"open-circuit potential": "MSMR", "intercalation kinetics": "MSMR"} + ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"particle": "MSMR", "intercalation kinetics": "MSMR"} + ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + "number of MSMR reactions": "1.5", + } + ) + def test_build_twice(self): model = pybamm.lithium_ion.SPM() # need to pick a model to set vars and build with self.assertRaisesRegex(pybamm.ModelError, "Model already built"): @@ -416,6 +446,7 @@ def test_print_options(self): with io.StringIO() as buffer, redirect_stdout(buffer): BatteryModelOptions(OPTIONS_DICT).print_options() output = buffer.getvalue() + self.assertEqual(output, PRINT_OPTIONS_OUTPUT) def test_option_phases(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py index aa62179e05..ec280cdd1f 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py @@ -27,6 +27,13 @@ def test_incompatible_options(self): pybamm.lead_acid.BaseModel({"SEI": "constant"}) with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"): pybamm.lead_acid.BaseModel({"lithium plating": "reversible"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.lead_acid.BaseModel( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + ) if __name__ == "__main__": diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 5cc0e72f1d..6815698588 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -368,6 +368,16 @@ def test_well_posed_current_sigmoid_ocp(self): options = {"open-circuit potential": "current sigmoid"} self.check_well_posedness(options) + def test_well_posed_msmr(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + "surface form": "differential", + } + self.check_well_posedness(options) + def test_well_posed_current_sigmoid_exchange_current(self): options = {"exchange-current density": "current sigmoid"} self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index 51a9b88d69..d7e95247e0 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -43,6 +43,16 @@ def test_well_posed_external_circuit_explicit_resistance(self): options = {"operating mode": "explicit resistance"} self.check_well_posedness(options) + def test_well_posed_msmr_with_psd(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "particle size": "distribution", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + self.check_well_posedness(options) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index b1c3209096..c305b21fee 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -156,6 +156,92 @@ def test_error(self): esoh_solver.solve(inputs) +class TestElectrodeSOHMSMR(TestCase): + def test_known_solution(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options=options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, options=options + ) + + Vmin = 2.8 + Vmax = 4.2 + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + Q_Li = parameter_values.evaluate(param.Q_Li_particles_init) + + inputs = {"Q_Li": Q_Li, "Q_n": Q_n, "Q_p": Q_p} + + # Solve the model and check outputs + sol = esoh_solver.solve(inputs) + + self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5) + self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5) + self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5) + + # Solve with split esoh and check outputs + ics = esoh_solver._set_up_solve(inputs) + sol_split = esoh_solver._solve_split(inputs, ics) + for key in sol: + if key != "Maximum theoretical energy [W.h]": + self.assertAlmostEqual(sol[key], sol_split[key].data[0], places=5) + + # Check feasibility checks can be performed successfully + esoh_solver._check_esoh_feasible(inputs) + + def test_known_solution_cell_capacity(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, known_value="cell capacity", options=options + ) + + Vmin = 2.8 + Vmax = 4.2 + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + Q = parameter_values.evaluate(param.Q) + + inputs = {"Q": Q, "Q_n": Q_n, "Q_p": Q_p} + + # Solve the model and check outputs + sol = esoh_solver.solve(inputs) + + self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5) + self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5) + self.assertAlmostEqual(sol["Q"], Q, places=5) + + def test_error(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, known_value="cell capacity", options=options + ) + with self.assertRaisesRegex(ValueError, "solve_for must be "): + esoh_solver._get_electrode_soh_sims_split() + + class TestElectrodeSOHHalfCell(TestCase): def test_known_solution(self): model = pybamm.lithium_ion.ElectrodeSOHHalfCell("positive") @@ -187,7 +273,7 @@ def test_efficiency(self): ) ) # Real energy should be less than discharge energy, - # and both should be greater than 0 + # and both should be greater than 0 self.assertLess(discharge_energy, theoretical_energy) self.assertLess(0, discharge_energy) self.assertLess(0, theoretical_energy) @@ -237,6 +323,16 @@ def test_min_max_stoich(self): V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) self.assertAlmostEqual(V, 2.8) + x0, x100, y100, y0 = pybamm.lithium_ion.get_min_max_stoichiometries( + parameter_values, + param, + known_value="cell capacity", + ) + V = parameter_values.evaluate(param.p.prim.U(y100, T) - param.n.prim.U(x100, T)) + self.assertAlmostEqual(V, 4.2) + V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) + self.assertAlmostEqual(V, 2.8) + def test_initial_soc_cell_capacity(self): param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Mohtat2020") @@ -263,6 +359,74 @@ def test_error(self): pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values) +class TestGetInitialOCP(TestCase): + def test_get_initial_ocp(self): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + Un, Up = pybamm.lithium_ion.get_initial_ocps(1, parameter_values, param) + self.assertAlmostEqual(Up - Un, 4.2) + Un, Up = pybamm.lithium_ion.get_initial_ocps(0, parameter_values, param) + self.assertAlmostEqual(Up - Un, 2.8) + Un, Up = pybamm.lithium_ion.get_initial_ocps("4 V", parameter_values, param) + self.assertAlmostEqual(Up - Un, 4) + + def test_min_max_ocp(self): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + +class TestGetInitialOCPMSMR(TestCase): + def test_get_initial_ocp(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + Un, Up = pybamm.lithium_ion.get_initial_ocps( + 1, parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 4.2, places=5) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + 0, parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 2.8, places=5) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + "4 V", parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 4) + + def test_min_max_ocp(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param, options=options + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param, known_value="cell capacity", options=options + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + if __name__ == "__main__": print("Add -v for more debug output") import sys diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 7dac0694a5..442817e354 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -108,6 +108,16 @@ def test_stress_induced_diffusion_not_implemented(self): with self.assertRaises(NotImplementedError): pybamm.lithium_ion.MPM(options) + def test_msmr(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + class TestMPMExternalCircuits(TestCase): def test_well_posed_voltage(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py new file mode 100644 index 0000000000..96369fbac2 --- /dev/null +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py @@ -0,0 +1,22 @@ +# +# Tests for the lithium-ion MSMR model +# +from tests import TestCase +import pybamm +import unittest + + +class TestMSMR(TestCase): + def test_well_posed(self): + model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) + model.check_well_posedness() + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + pybamm.settings.debug_mode = True + unittest.main() diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py index 8288655b87..2559641d7e 100644 --- a/tests/unit/test_parameters/test_bpx.py +++ b/tests/unit/test_parameters/test_bpx.py @@ -9,7 +9,6 @@ import pybamm import copy - class TestBPX(TestCase): def setUp(self): self.base = { @@ -161,7 +160,26 @@ def test_constant_functions(self): json.dump(bpx_obj, tmp) tmp.flush() - pybamm.ParameterValues.create_from_bpx(tmp.name) + param = pybamm.ParameterValues.create_from_bpx(tmp.name) + + # Function to check that functional parameters output constants + def check_constant_output(func): + stos = [0, 1] + T = 298.15 + p_vals = [func(sto, T) for sto in stos] + self.assertEqual(p_vals[0], p_vals[1]) + + for electrode in ["Negative", "Positive"]: + D = param[f"{electrode} electrode diffusivity [m2.s-1]"] + dUdT = param[f"{electrode} electrode OCP entropic change [V.K-1]"] + check_constant_output(D) + check_constant_output(dUdT) + + kappa = param["Electrolyte conductivity [S.m-1]"] + De = param["Electrolyte diffusivity [m2.s-1]"] + check_constant_output(kappa) + check_constant_output(De) + def test_table_data(self): bpx_obj = copy.copy(self.base) @@ -222,6 +240,66 @@ def test_bpx_soc_error(self): with self.assertRaisesRegex(ValueError, "Target SOC"): pybamm.ParameterValues.create_from_bpx("blah.json", target_soc=10) + def test_bpx_arrhenius(self): + bpx_obj = copy.copy(self.base) + + filename = "tmp.json" + with tempfile.NamedTemporaryFile( + suffix=filename, delete=False, mode="w" + ) as tmp: + # write to a tempory file so we can + # get the source later on using inspect.getsource + # (as long as the file still exists) + json.dump(bpx_obj, tmp) + tmp.flush() + + pv = pybamm.ParameterValues.create_from_bpx(tmp.name) + + + def arrhenius_assertion(pv, param_key, Ea_key): + sto = 0.5 + T = 300 + c_e = 1000 + c_s_surf = 15000 + c_s_max = 20000 + T_ref = pv["Reference temperature [K]"] + Ea = pv[Ea_key] + + if "exchange-current" in param_key: + eval_ratio = ( + pv[param_key](c_e, c_s_surf, c_s_max, T).value + / pv[param_key](c_e, c_s_surf, c_s_max, T_ref).value + ) + else: + eval_ratio = ( + pv[param_key](sto, T).value + / pv[param_key](sto, T_ref).value + ) + + calc_ratio = pybamm.exp(Ea / pybamm.constants.R * (1 / T_ref - 1 / T)).value + + self.assertAlmostEqual(eval_ratio, calc_ratio) + + param_keys = [ + "Electrolyte conductivity [S.m-1]", + "Electrolyte diffusivity [m2.s-1]", + "Negative electrode diffusivity [m2.s-1]", + "Positive electrode diffusivity [m2.s-1]", + "Positive electrode exchange-current density [A.m-2]", + "Negative electrode exchange-current density [A.m-2]", + ] + + Ea_keys = [ + "Electrolyte conductivity activation energy [J.mol-1]", + "Electrolyte diffusivity activation energy [J.mol-1]", + "Negative electrode diffusivity activation energy [J.mol-1]", + "Positive electrode diffusivity activation energy [J.mol-1]", + "Positive electrode reaction rate constant activation energy [J.mol-1]", + "Negative electrode reaction rate constant activation energy [J.mol-1]", + ] + + for param_key, Ea_key in zip(param_keys, Ea_keys): + arrhenius_assertion(pv, param_key, Ea_key) if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index def4c339f3..c6a4831e86 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -119,6 +119,25 @@ def test_set_initial_stoichiometries(self): y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + def test_set_initial_ocps(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param_100 = pybamm.ParameterValues("MSMR_Example") + param_100.set_initial_ocps(1, inplace=True, options=options) + param_0 = param_100.set_initial_ocps(0, inplace=False, options=options) + + Un_0 = param_0["Initial voltage in negative electrode [V]"] + Up_0 = param_0["Initial voltage in positive electrode [V]"] + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + Un_100 = param_100["Initial voltage in negative electrode [V]"] + Up_100 = param_100["Initial voltage in positive electrode [V]"] + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + def test_check_parameter_values(self): with self.assertRaisesRegex(ValueError, "propotional term"): pybamm.ParameterValues( diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 83ec42ef6c..d0926e5c94 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -203,6 +203,13 @@ def test_solve_with_initial_soc(self): sim.build(initial_soc=0.5) self.assertEqual(sim._built_initial_soc, 0.5) + # test with MSMR + model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) + param = pybamm.ParameterValues("MSMR_Example") + sim = pybamm.Simulation(model, parameter_values=param) + sim.build(initial_soc=0.5) + self.assertEqual(sim._built_initial_soc, 0.5) + def test_solve_with_inputs(self): model = pybamm.lithium_ion.SPM() param = model.default_parameter_values diff --git a/tests/unit/test_solvers/test_scipy_solver.py b/tests/unit/test_solvers/test_scipy_solver.py index e8a0d1d120..fad6651d55 100644 --- a/tests/unit/test_solvers/test_scipy_solver.py +++ b/tests/unit/test_solvers/test_scipy_solver.py @@ -338,35 +338,34 @@ def test_model_solver_multiple_inputs_initial_conditions_error(self): ): solver.solve(model, t_eval, inputs=inputs_list, nproc=2) - def test_model_solver_multiple_inputs_jax_format_error(self): - # Create model - model = pybamm.BaseModel() - model.convert_to_format = "jax" - domain = ["negative electrode", "separator", "positive electrode"] - var = pybamm.Variable("var", domain=domain) - model.rhs = {var: -pybamm.InputParameter("rate") * var} - model.initial_conditions = {var: 2 * pybamm.InputParameter("rate")} - # No need to set parameters; can use base discretisation (no spatial - # operators) - # create discretisation - mesh = get_mesh_for_testing() - spatial_methods = {"macroscale": pybamm.FiniteVolume()} - disc = pybamm.Discretisation(mesh, spatial_methods) - disc.process_model(model) + def test_model_solver_multiple_inputs_jax_format(self): + if pybamm.have_jax(): + # Create model + model = pybamm.BaseModel() + model.convert_to_format = "jax" + domain = ["negative electrode", "separator", "positive electrode"] + var = pybamm.Variable("var", domain=domain) + model.rhs = {var: -pybamm.InputParameter("rate") * var} + model.initial_conditions = {var: 1} + # create discretisation + mesh = get_mesh_for_testing() + spatial_methods = {"macroscale": pybamm.FiniteVolume()} + disc = pybamm.Discretisation(mesh, spatial_methods) + disc.process_model(model) - solver = pybamm.ScipySolver(rtol=1e-8, atol=1e-8, method="RK45") - t_eval = np.linspace(0, 10, 100) - ninputs = 8 - inputs_list = [{"rate": 0.01 * (i + 1)} for i in range(ninputs)] + solver = pybamm.JaxSolver(rtol=1e-8, atol=1e-8, method="RK45") + t_eval = np.linspace(0, 10, 100) + ninputs = 8 + inputs_list = [{"rate": 0.01 * (i + 1)} for i in range(ninputs)] - with self.assertRaisesRegex( - pybamm.SolverError, - ( - "Cannot solve list of inputs with multiprocessing " - 'when model in format "jax".' - ), - ): - solver.solve(model, t_eval, inputs=inputs_list, nproc=2) + solutions = solver.solve(model, t_eval, inputs=inputs_list, nproc=2) + for i in range(ninputs): + with self.subTest(i=i): + solution = solutions[i] + np.testing.assert_array_equal(solution.t, t_eval) + np.testing.assert_allclose( + solution.y[0], np.exp(-0.01 * (i + 1) * solution.t) + ) def test_model_solver_with_event_with_casadi(self): # Create model