From d627b1a0a6b55938ef05fbefad3b93a1a55b6787 Mon Sep 17 00:00:00 2001 From: amirDahari1 Date: Wed, 29 Mar 2023 15:21:33 +0100 Subject: [PATCH 01/35] log transport efficiency --- .../transport_efficiency/__init__.py | 1 + .../log_transport_efficiency.py | 45 +++++++++++++++++++ 2 files changed, 46 insertions(+) create mode 100644 pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index 13fbe8487d..57af93a6c2 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,2 +1,3 @@ from .base_transport_efficiency import BaseModel from .bruggeman_transport_efficiency import Bruggeman +from .log_transport_efficiency import LogTransport diff --git a/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py new file mode 100644 index 0000000000..ed40621ad4 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py @@ -0,0 +1,45 @@ +# +# Class for Bruggemantransport_efficiency +# +import pybamm +import numpy as np +from .base_transport_efficiency import BaseModel + + +class LogTransport(BaseModel): + """Submodel for LogTransport transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_dict[domain] = 1-np.log(eps_k) + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + tor_k = 1-np.log(eps_k) + tor_dict[domain] = tor_k + + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From 41b658468a642e924c4571e2e3ecfef24be177b1 Mon Sep 17 00:00:00 2001 From: "Tom.Maull" Date: Wed, 29 Mar 2023 15:23:37 +0100 Subject: [PATCH 02/35] logsqrt_transport --- .../logsqrt_transport_efficiency.py | 44 +++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py diff --git a/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py new file mode 100644 index 0000000000..9ef00a03a8 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py @@ -0,0 +1,44 @@ +# +# Class for LogSqrt_transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class LogSqrt(BaseModel): + """Submodel for LogSqrt transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_dict[domain] = 1-ln(eps_k**.5) + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + tor_k = 1-ln(eps_k**.5) + tor_dict[domain] = tor_k + + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From 442a2eafbc7552ad29f92c798fb07705d1055ed6 Mon Sep 17 00:00:00 2001 From: Julia Wind Date: Wed, 29 Mar 2023 16:25:30 +0200 Subject: [PATCH 03/35] adding linear transport efficiency --- .../notebooks/models/tortuosity_models.ipynb | 126 ++++++++++++++++++ .../transport_efficiency/__init__.py | 1 + .../linear_transport_efficiency.py | 44 ++++++ 3 files changed, 171 insertions(+) create mode 100644 examples/notebooks/models/tortuosity_models.ipynb create mode 100644 pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py diff --git a/examples/notebooks/models/tortuosity_models.ipynb b/examples/notebooks/models/tortuosity_models.ipynb new file mode 100644 index 0000000000..efb3a0c996 --- /dev/null +++ b/examples/notebooks/models/tortuosity_models.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transport Efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pybamm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model_lin = pybamm.lithium_ion.DFN(build=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model_lin.submodels[\"electrolyte transport efficiency\"]= pybamm.transport_efficiency.Linear(param=model_lin.param, component=\"Electrolyte\")\n", + "model_lin.submodels[\"electrode transport efficiency\"]= pybamm.transport_efficiency.Linear(param=model_lin.param, component=\"Electrode\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model_lin.build_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "simulation_lin = pybamm.Simulation(model_lin)\n", + "sol_lin=simulation_lin.solve([0, 3600])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.DFN()\n", + "simulation = pybamm.Simulation(model)\n", + "sol=simulation.solve([0, 3600])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "67b518af7d4348aaa697f6bb3267a1ff", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pybamm.dynamic_plot([sol_lin,sol],labels=[\"Linear\",\"Standard\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pbtort", + "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": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index 13fbe8487d..25fde5e6f0 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,2 +1,3 @@ from .base_transport_efficiency import BaseModel from .bruggeman_transport_efficiency import Bruggeman +from .linear_transport_efficiency import Linear \ No newline at end of file diff --git a/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py new file mode 100644 index 0000000000..5ea1f39478 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py @@ -0,0 +1,44 @@ +# +# Class for linear transport efficiency 2-eps +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class Linear(BaseModel): + """Submodel for linear transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_dict[domain] = 2-eps_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + tor_k = 2-eps_k + tor_dict[domain] = tor_k + + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From a94f7386594be07a60628c5d497b4910c619a36c Mon Sep 17 00:00:00 2001 From: Ruimin-S Date: Wed, 29 Mar 2023 15:34:03 +0100 Subject: [PATCH 04/35] half_transport_effciency added --- .../transport_efficiency/__init__.py | 1 + .../half_transport_efficiency.py | 44 +++++++++++++++++++ 2 files changed, 45 insertions(+) create mode 100644 pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index 13fbe8487d..21f1aa7be2 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,2 +1,3 @@ from .base_transport_efficiency import BaseModel from .bruggeman_transport_efficiency import Bruggeman +from .half_transport_efficiency import HalfTransfer diff --git a/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py new file mode 100644 index 0000000000..daf48191bf --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py @@ -0,0 +1,44 @@ +# +# Class for Bruggemantransport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class HalfTransfer(BaseModel): + """Submodel for transport_efficiency = (3 - eps_k) / 2 + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_dict[domain] = (3 - eps_k) / 2 + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + tor_k = (3 - eps_k) / 2 + tor_dict[domain] = tor_k + + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From ca330838fc1bae51343211e051d47bf8365a17c9 Mon Sep 17 00:00:00 2001 From: isaacsquires Date: Wed, 29 Mar 2023 15:48:00 +0100 Subject: [PATCH 05/35] add tortuosity factor submodel --- .../transport_efficiency/__init__.py | 1 + .../transport_efficiency/tortuosity_factor.py | 46 +++++++++++++++++++ 2 files changed, 47 insertions(+) create mode 100644 pybamm/models/submodels/transport_efficiency/tortuosity_factor.py diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index b6a9db7c78..a8f5e84597 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -3,3 +3,4 @@ from .half_transport_efficiency import HalfTransfer from .linear_transport_efficiency import Linear from .log_transport_efficiency import LogTransport +from .tortuosity_factor import TortuosityFactor diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py new file mode 100644 index 0000000000..2cd18d81aa --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -0,0 +1,46 @@ +# +# Class for Bruggemantransport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class TortuosityFactor(BaseModel): + """Submodel for tortuosity factor transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tau_k = self.param.domain_params[domain.split()[0]].tau_e + tor_dict[domain] = eps_k / tau_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + tau_k = self.param.domain_params[domain.split()[0]].tau_s + tor_k = eps_k / tau_k + tor_dict[domain] = tor_k + + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From 1bf3b48ba9eaa8d31aaa171a3273468a7cbb4eb1 Mon Sep 17 00:00:00 2001 From: isaacsquires Date: Wed, 29 Mar 2023 16:37:02 +0100 Subject: [PATCH 06/35] add multiple tortuosity models --- .../full_battery_models/base_battery_model.py | 14 +++- .../transport_efficiency/__init__.py | 5 +- .../general_transport_efficiency.py | 70 +++++++++++++++++++ .../half_transport_efficiency.py | 44 ------------ .../linear_transport_efficiency.py | 44 ------------ .../log_transport_efficiency.py | 45 ------------ .../logsqrt_transport_efficiency.py | 44 ------------ .../transport_efficiency/tortuosity_factor.py | 46 ------------ pybamm/parameters/geometric_parameters.py | 7 ++ pybamm/parameters/lithium_ion_parameters.py | 2 + 10 files changed, 92 insertions(+), 229 deletions(-) create mode 100644 pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py delete mode 100644 pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py delete mode 100644 pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py delete mode 100644 pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py delete mode 100644 pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py delete mode 100644 pybamm/models/submodels/transport_efficiency/tortuosity_factor.py diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index e82f3d5868..eeefd8278e 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -199,6 +199,14 @@ def __init__(self, extra_options): "composite", "integrated", ], + "transport efficiency": [ + "Bruggeman", + "log square root", + "log", + "linear", + "tortuosity factor", + "half volume fraction", + ], "hydrolysis": ["false", "true"], "intercalation kinetics": [ "symmetric Butler-Volmer", @@ -1017,12 +1025,14 @@ def set_external_circuit_submodel(self): def set_transport_efficiency_submodels(self): self.submodels[ "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( + ] = pybamm.transport_efficiency.GeneralTransportEfficiency( self.param, "Electrolyte", self.options ) self.submodels[ "electrode transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman(self.param, "Electrode", self.options) + ] = pybamm.transport_efficiency.GeneralTransportEfficiency( + self.param, "Electrode", self.options + ) def set_thermal_submodel(self): if self.options["thermal"] == "isothermal": diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index a8f5e84597..dd1ecb42cd 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,6 +1,3 @@ from .base_transport_efficiency import BaseModel from .bruggeman_transport_efficiency import Bruggeman -from .half_transport_efficiency import HalfTransfer -from .linear_transport_efficiency import Linear -from .log_transport_efficiency import LogTransport -from .tortuosity_factor import TortuosityFactor +from .general_transport_efficiency import GeneralTransportEfficiency diff --git a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py new file mode 100644 index 0000000000..3e79bf542b --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py @@ -0,0 +1,70 @@ +# +# Class for Bruggemantransport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class GeneralTransportEfficiency(BaseModel): + """Submodel for Bruggeman transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def _transport_efficiency_models(self, eps_k): + if self.options["transport efficiency"] == "log square root": + tor_k = 1 - pybamm.Log(eps_k**0.5) + elif self.options["transport efficiency"] == "log": + tor_k = 1 - pybamm.Log(eps_k) + elif self.options["transport efficiency"] == "linear": + tor_k = 2 - eps_k + elif self.options["transport efficiency"] == "half volume fraction": + tor_k = 1.5 - eps_k / 2 + + return tor_k + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + if self.options["transport efficiency"] == "Bruggeman": + b_k = self.param.domain_params[domain.split()[0]].b_e + tor_k = eps_k**b_k + elif self.options["transport efficiency"] == "tortuosity factor": + tau_k = self.param.domain_params[domain.split()[0]].tau_e + tor_k = eps_k / tau_k + else: + tor_k = self._transport_efficiency_models(eps_k) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} active material volume fraction"] + if self.options["transport efficiency"] == "Bruggeman": + b_k = self.param.domain_params[domain.split()[0]].b_s + tor_k = eps_k**b_k + elif self.options["transport efficiency"] == "tortuosity factor": + tau_k = self.param.domain_params[domain.split()[0]].tau_s + tor_k = eps_k / tau_k + else: + tor_k = self._transport_efficiency_models(eps_k) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py deleted file mode 100644 index daf48191bf..0000000000 --- a/pybamm/models/submodels/transport_efficiency/half_transport_efficiency.py +++ /dev/null @@ -1,44 +0,0 @@ -# -# Class for Bruggemantransport_efficiency -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class HalfTransfer(BaseModel): - """Submodel for transport_efficiency = (3 - eps_k) / 2 - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - tor_dict[domain] = (3 - eps_k) / 2 - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - tor_k = (3 - eps_k) / 2 - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py deleted file mode 100644 index 5ea1f39478..0000000000 --- a/pybamm/models/submodels/transport_efficiency/linear_transport_efficiency.py +++ /dev/null @@ -1,44 +0,0 @@ -# -# Class for linear transport efficiency 2-eps -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class Linear(BaseModel): - """Submodel for linear transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - tor_dict[domain] = 2-eps_k - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - tor_k = 2-eps_k - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py deleted file mode 100644 index ed40621ad4..0000000000 --- a/pybamm/models/submodels/transport_efficiency/log_transport_efficiency.py +++ /dev/null @@ -1,45 +0,0 @@ -# -# Class for Bruggemantransport_efficiency -# -import pybamm -import numpy as np -from .base_transport_efficiency import BaseModel - - -class LogTransport(BaseModel): - """Submodel for LogTransport transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - tor_dict[domain] = 1-np.log(eps_k) - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - tor_k = 1-np.log(eps_k) - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py deleted file mode 100644 index 9ef00a03a8..0000000000 --- a/pybamm/models/submodels/transport_efficiency/logsqrt_transport_efficiency.py +++ /dev/null @@ -1,44 +0,0 @@ -# -# Class for LogSqrt_transport_efficiency -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class LogSqrt(BaseModel): - """Submodel for LogSqrt transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - tor_dict[domain] = 1-ln(eps_k**.5) - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - tor_k = 1-ln(eps_k**.5) - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py deleted file mode 100644 index 2cd18d81aa..0000000000 --- a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py +++ /dev/null @@ -1,46 +0,0 @@ -# -# Class for Bruggemantransport_efficiency -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class TortuosityFactor(BaseModel): - """Submodel for tortuosity factor transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - tau_k = self.param.domain_params[domain.split()[0]].tau_e - tor_dict[domain] = eps_k / tau_k - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - tau_k = self.param.domain_params[domain.split()[0]].tau_s - tor_k = eps_k / tau_k - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py index fcfc058c80..f303a9c46b 100644 --- a/pybamm/parameters/geometric_parameters.py +++ b/pybamm/parameters/geometric_parameters.py @@ -66,6 +66,7 @@ def _set_parameters(self): if self.domain == "separator": self.L = pybamm.Parameter("Separator thickness [m]") self.b_e = pybamm.Parameter("Separator Bruggeman coefficient (electrolyte)") + self.tau_e = pybamm.Parameter("Separator tortuosity factor (electrolyte)") return Domain = self.domain.capitalize() @@ -87,6 +88,12 @@ def _set_parameters(self): self.b_s = pybamm.Parameter( f"{Domain} electrode Bruggeman coefficient (electrode)" ) + self.tau_e = pybamm.Parameter( + f"{Domain} electrode tortuosity factor (electrolyte)" + ) + self.tau_s = pybamm.Parameter( + f"{Domain} electrode tortuosity factor (electrode)" + ) class ParticleGeometricParameters(BaseParameters): diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 43901eff2b..6cf1ef4ee4 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -238,6 +238,7 @@ def _set_parameters(self): # Parameters that appear in the separator self.b_e = self.geo.b_e + self.tau_e = self.geo.tau_e self.L = self.geo.L # Thermal @@ -295,6 +296,7 @@ def _set_parameters(self): # Tortuosity parameters self.b_s = self.geo.b_s + self.tau_s = self.geo.tau_s self.C_dl = pybamm.Parameter( f"{Domain} electrode double-layer capacity [F.m-2]" From f5136270b2adf1400a072e33e0a96ee50e90ba7e Mon Sep 17 00:00:00 2001 From: isaacsquires Date: Wed, 29 Mar 2023 16:46:02 +0100 Subject: [PATCH 07/35] add tortuosity factor to model examples --- .../notebooks/models/tortuosity_models.ipynb | 62 +++++++------------ 1 file changed, 22 insertions(+), 40 deletions(-) diff --git a/examples/notebooks/models/tortuosity_models.ipynb b/examples/notebooks/models/tortuosity_models.ipynb index efb3a0c996..b6f5828128 100644 --- a/examples/notebooks/models/tortuosity_models.ipynb +++ b/examples/notebooks/models/tortuosity_models.ipynb @@ -23,58 +23,40 @@ "metadata": {}, "outputs": [], "source": [ - "model_lin = pybamm.lithium_ion.DFN(build=False)" + "sols = []\n", + "labels = ['log square root', 'log', 'linear', 'half volume fraction', 'Bruggeman']\n", + "for t_label in labels:\n", + " model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model\n", + " sim = pybamm.Simulation(model)\n", + " sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "model_lin.submodels[\"electrolyte transport efficiency\"]= pybamm.transport_efficiency.Linear(param=model_lin.param, component=\"Electrolyte\")\n", - "model_lin.submodels[\"electrode transport efficiency\"]= pybamm.transport_efficiency.Linear(param=model_lin.param, component=\"Electrode\")" + "model = pybamm.lithium_ion.DFN(options={'transport efficiency': 'tortuosity factor'}) # Doyle-Fuller-Newman model\n", + "parameter_values = model.default_parameter_values\n", + "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 1.5,\n", + " 'Positive electrode tortuosity factor (electrolyte)': 1.5,\n", + " 'Negative electrode tortuosity factor (electrode)': 1.5,\n", + " 'Positive electrode tortuosity factor (electrode)': 1.5,\n", + " 'Separator tortuosity factor (electrolyte)': 1.2}, check_already_exists=False)\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model_lin.build_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_lin = pybamm.Simulation(model_lin)\n", - "sol_lin=simulation_lin.solve([0, 3600])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.lithium_ion.DFN()\n", - "simulation = pybamm.Simulation(model)\n", - "sol=simulation.solve([0, 3600])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67b518af7d4348aaa697f6bb3267a1ff", + "model_id": "47293c6a70a349ca922d1484adceebf8", "version_major": 2, "version_minor": 0 }, @@ -88,16 +70,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pybamm.dynamic_plot([sol_lin,sol],labels=[\"Linear\",\"Standard\"])" + "pybamm.dynamic_plot(sols,labels=labels+['Tortuosity factor'])" ] } ], @@ -117,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.1" }, "orig_nbformat": 4 }, From 14c0ced1e72a41fbe5998eb79c16aa8c8955e4e6 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 11 Oct 2023 16:00:42 +0100 Subject: [PATCH 08/35] Update submodel options and add citations. Also change the solid phase volume fraction to be (1-porosity) rather than the active material volume fraction --- CHANGELOG.md | 1 + .../notebooks/models/tortuosity_models.ipynb | 212 ++++++++++++++++-- pybamm/CITATIONS.bib | 70 ++++++ .../full_battery_models/base_battery_model.py | 18 +- .../general_transport_efficiency.py | 38 ++-- .../test_base_battery_model.py | 1 + 6 files changed, 301 insertions(+), 39 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 421d3bfa29..131e8ee527 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,7 @@ # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31 ## Features +- Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - 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)) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index 1617dcb995..ca0b1c5553 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -7,13 +7,94 @@ "# Transport Efficiency" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pybamm\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Bruggeman', 'ordered packing', 'hyperbola of revolution', 'overlapping spheres', 'tortuosity factor', 'random overlapping cylinders', 'heterogeneous catalyst', 'cation-exchange membrane']\n" + ] + } + ], + "source": [ + "sols = []\n", + "te_opts = pybamm.BatteryModelOptions({}).possible_options[\"transport efficiency\"]\n", + "parameter_values = pybamm.ParameterValues(\"Marquis2019\")\n", + "print(te_opts)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative electrode porosity\t0.3\n", + "Positive electrode porosity\t0.3\n", + "Separator porosity\t1.0\n" + ] + } + ], + "source": [ + "parameter_values.search(\"porosity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative electrode Bruggeman coefficient (electrode)\t1.5\n", + "Negative electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Positive electrode Bruggeman coefficient (electrode)\t1.5\n", + "Positive electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Separator Bruggeman coefficient (electrolyte)\t1.5\n" + ] + } + ], + "source": [ + "parameter_values.search(\"Bruggeman\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add tortuosity factors that replicate the Bruggeman values" + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "import pybamm" + "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", + " 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", + " 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", + " 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", + " 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False)" ] }, { @@ -22,11 +103,9 @@ "metadata": {}, "outputs": [], "source": [ - "sols = []\n", - "labels = ['log square root', 'log', 'linear', 'half volume fraction', 'Bruggeman']\n", - "for t_label in labels:\n", + "for t_label in te_opts:\n", " model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model\n", - " sim = pybamm.Simulation(model)\n", + " sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", " sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] }, @@ -34,28 +113,89 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "856222356e2c448c990304033c81e410", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pybamm.dynamic_plot(sols,labels=te_opts)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(sols[0][\"Terminal voltage [V]\"].data, sols[4][\"Terminal voltage [V]\"].data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN(options={'transport efficiency': 'tortuosity factor'}) # Doyle-Fuller-Newman model\n", - "parameter_values = model.default_parameter_values\n", - "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 1.5,\n", - " 'Positive electrode tortuosity factor (electrolyte)': 1.5,\n", - " 'Negative electrode tortuosity factor (electrode)': 1.5,\n", - " 'Positive electrode tortuosity factor (electrode)': 1.5,\n", - " 'Separator tortuosity factor (electrolyte)': 1.2}, check_already_exists=False)\n", + "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0,\n", + " 'Positive electrode tortuosity factor (electrolyte)': 4.0,\n", + " 'Negative electrode tortuosity factor (electrode)': 3.0,\n", + " 'Positive electrode tortuosity factor (electrode)': 3.0,\n", + " 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.DFN(options={'transport efficiency': \"tortuosity factor\"}) # Doyle-Fuller-Newman model\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sols.append(sim.solve([0, 3600])) # solve for 1 hour" + "sols.append(sim.solve([0, 3600]))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb0c96127aae440698a5b373282032a8", + "model_id": "85ea5ac088354ca6add0e5b83cf0097d", "version_major": 2, "version_minor": 0 }, @@ -69,17 +209,53 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pybamm.dynamic_plot(sols,labels=labels+['Tortuosity factor'])" + "pybamm.dynamic_plot(sols,labels=te_opts+[\"higher tortuosity factor\"])" ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] KA Akanni, JW Evans, and IS Abramson. Effective transport coefficients in heterogeneous media. Chemical Engineering Science, 42(8):1945–1954, 1987.\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] JW Beeckman. Mathematical description of heterogeneous materials. Chemical engineering science, 45(8):2603–2610, 1990.\n", + "[4] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[5] 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", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan 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", + "[7] JS Mackie and P Meares. The diffusion of electrolytes in a cation-exchange resin membrane i. theoretical. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 232(1191):498–509, 1955.\n", + "[8] 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", + "[9] EE Petersen. Diffusion in a pore of varying cross section. AIChE Journal, 4(3):343–345, 1958.\n", + "[10] 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", + "[11] Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random arrays of freely overlapping cylinders with various orientation distributions. The Journal of chemical physics, 98(1):616–626, 1993.\n", + "[12] Harold L Weissberg. Effective diffusion coefficient in porous media. Journal of Applied Physics, 34(9):2636–2639, 1963.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index 21740584b5..885325c271 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -704,3 +704,73 @@ @article{landesfeind2019temperature year={2019}, publisher={The Electrochemical Society} } +@article{akanni1987effective, + title={Effective transport coefficients in heterogeneous media}, + author={Akanni, KA and Evans, JW and Abramson, IS}, + journal={Chemical Engineering Science}, + volume={42}, + number={8}, + pages={1945--1954}, + year={1987}, + publisher={Elsevier} +} +@article{petersen1958diffusion, + title={Diffusion in a pore of varying cross section}, + author={Petersen, EE}, + journal={AIChE Journal}, + volume={4}, + number={3}, + pages={343--345}, + year={1958}, + publisher={Wiley Online Library} +} +@article{bruggeman1935berechnung, + title={Berechnung verschiedener physikalischer Konstanten von heterogenen Substanzen. I. Dielektrizit{\"a}tskonstanten und Leitf{\"a}higkeiten der Mischk{\"o}rper aus isotropen Substanzen}, + author={Bruggeman, Von DAG}, + journal={Annalen der physik}, + volume={416}, + number={7}, + pages={636--664}, + year={1935}, + publisher={Wiley Online Library} +} +@article{weissberg1963effective, + title={Effective diffusion coefficient in porous media}, + author={Weissberg, Harold L}, + journal={Journal of Applied Physics}, + volume={34}, + number={9}, + pages={2636--2639}, + year={1963}, + publisher={American Institute of Physics} +} +@article{tomadakis1993transport, + title={Transport properties of random arrays of freely overlapping cylinders with various orientation distributions}, + author={Tomadakis, Manolis M and Sotirchos, Stratis V}, + journal={The Journal of chemical physics}, + volume={98}, + number={1}, + pages={616--626}, + year={1993}, + publisher={American Institute of Physics} +} +@article{beeckman1990mathematical, + title={Mathematical description of heterogeneous materials}, + author={Beeckman, JW}, + journal={Chemical engineering science}, + volume={45}, + number={8}, + pages={2603--2610}, + year={1990}, + publisher={Elsevier} +} +@article{mackie1955diffusion, + title={The diffusion of electrolytes in a cation-exchange resin membrane I. Theoretical}, + author={Mackie, JS and Meares, P}, + journal={Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences}, + volume={232}, + number={1191}, + pages={498--509}, + year={1955}, + publisher={The Royal Society London} +} diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 8e930083ab..6afaa642f8 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -237,14 +237,6 @@ def __init__(self, extra_options): "integrated", ], "exchange-current density": ["single", "current sigmoid"], - "transport efficiency": [ - "Bruggeman", - "log square root", - "log", - "linear", - "tortuosity factor", - "half volume fraction", - ], "hydrolysis": ["false", "true"], "intercalation kinetics": [ "symmetric Butler-Volmer", @@ -312,6 +304,16 @@ def __init__(self, extra_options): "surface form": ["false", "differential", "algebraic"], "thermal": ["isothermal", "lumped", "x-lumped", "x-full"], "total interfacial current density as a state": ["false", "true"], + "transport efficiency": [ + "Bruggeman", + "ordered packing", + "hyperbola of revolution", + "overlapping spheres", + "tortuosity factor", + "random overlapping cylinders", + "heterogeneous catalyst", + "cation-exchange membrane", + ], "working electrode": ["both", "positive"], "x-average side reactions": ["false", "true"], } diff --git a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py index 3e79bf542b..f446cb9643 100644 --- a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py @@ -21,15 +21,25 @@ class GeneralTransportEfficiency(BaseModel): def __init__(self, param, component, options=None): super().__init__(param, component, options=options) - def _transport_efficiency_models(self, eps_k): - if self.options["transport efficiency"] == "log square root": - tor_k = 1 - pybamm.Log(eps_k**0.5) - elif self.options["transport efficiency"] == "log": - tor_k = 1 - pybamm.Log(eps_k) - elif self.options["transport efficiency"] == "linear": + def _tortuosity_factor_model(self, eps_k): + if self.options["transport efficiency"] == "ordered packing": + pybamm.citations.register("akanni1987effective") + tor_k = (3 - eps_k)*0.5 + elif self.options["transport efficiency"] == "hyperbola of revolution": + pybamm.citations.register("petersen1958diffusion") tor_k = 2 - eps_k - elif self.options["transport efficiency"] == "half volume fraction": - tor_k = 1.5 - eps_k / 2 + elif self.options["transport efficiency"] == "overlapping spheres": + pybamm.citations.register("weissberg1963effective") + tor_k = 1 - pybamm.Log(eps_k*0.5) + elif self.options["transport efficiency"] == "random overlapping cylinders": + pybamm.citations.register("tomadakis1993transport") + tor_k = 1 - pybamm.Log(eps_k) + elif self.options["transport efficiency"] == "heterogeneous catalyst": + pybamm.citations.register("beeckman1990mathematical") + tor_k = eps_k / (1 - (1- eps_k)**(1/3)) + elif self.options["transport efficiency"] == "cation-exchange membrane": + pybamm.citations.register("mackie1955diffusion") + tor_k = ((2 - eps_k) / eps_k)**2 return tor_k @@ -40,13 +50,14 @@ def get_coupled_variables(self, variables): Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] if self.options["transport efficiency"] == "Bruggeman": + pybamm.citations.register("bruggeman1935berechnung") b_k = self.param.domain_params[domain.split()[0]].b_e tor_k = eps_k**b_k elif self.options["transport efficiency"] == "tortuosity factor": tau_k = self.param.domain_params[domain.split()[0]].tau_e tor_k = eps_k / tau_k else: - tor_k = self._transport_efficiency_models(eps_k) + tor_k = self._tortuosity_factor_model(eps_k) tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} @@ -55,15 +66,16 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] + phi_k = (1 - variables[f"{Domain} porosity"]) if self.options["transport efficiency"] == "Bruggeman": + pybamm.citations.register("bruggeman1935berechnung") b_k = self.param.domain_params[domain.split()[0]].b_s - tor_k = eps_k**b_k + tor_k = phi_k**b_k elif self.options["transport efficiency"] == "tortuosity factor": tau_k = self.param.domain_params[domain.split()[0]].tau_s - tor_k = eps_k / tau_k + tor_k = phi_k / tau_k else: - tor_k = self._transport_efficiency_models(eps_k) + tor_k = self._tortuosity_factor_model(phi_k) tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) 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 79c6d8a720..b30dfa9bbe 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 @@ -47,6 +47,7 @@ 'surface form': 'differential' (possible: ['false', 'differential', 'algebraic']) 'thermal': 'x-full' (possible: ['isothermal', 'lumped', 'x-lumped', 'x-full']) 'total interfacial current density as a state': 'false' (possible: ['false', 'true']) +'transport efficiency': 'Bruggeman' (possible: ['Bruggeman', 'ordered packing', 'hyperbola of revolution', 'overlapping spheres', 'tortuosity factor', 'random overlapping cylinders', 'heterogeneous catalyst', 'cation-exchange membrane']) 'working electrode': 'both' (possible: ['both', 'positive']) 'x-average side reactions': 'false' (possible: ['false', 'true']) """ # noqa: E501 From ad757e49fc23bd0500100f2f0c1ef6cdbd85ca5d Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 11 Oct 2023 17:31:44 +0100 Subject: [PATCH 09/35] Update notebook and index for docs, get rid of unnecessary Bruggeman file --- docs/source/examples/index.rst | 1 + .../notebooks/models/tortuosity_models.ipynb | 87 +++++++++++++++---- pybamm/CITATIONS.bib | 10 +++ .../transport_efficiency/__init__.py | 1 - .../bruggeman_transport_efficiency.py | 46 ---------- .../general_transport_efficiency.py | 1 + 6 files changed, 84 insertions(+), 62 deletions(-) delete mode 100644 pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index 7c17cfc4aa..878a30327e 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -70,6 +70,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/submodel_cracking_DFN_or_SPM.ipynb notebooks/models/loss_of_active_materials.ipynb notebooks/models/thermal-models.ipynb + notebooks/models/tortusity_models.ipynb notebooks/models/unsteady-heat-equation.ipynb notebooks/models/using-model-options_thermal-example.ipynb notebooks/models/using-submodels.ipynb diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index ca0b1c5553..e9e42fc447 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -4,7 +4,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Transport Efficiency" + "# Transport efficiency and the models for tortuosity factor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pybamm models utilize a ratio that we refer to as \"transport efficiency\" which can be applied to transport co-efficients such as the diffusivity in the electrolyte that relates the effective transport property through a porous media comprised of a conducting and non-conducting phase to that of the transport through the bulk of the conducting phase\n", + "$$\n", + "B = \\frac{X_{eff}}{X_0} = \\frac{\\epsilon}{\\tau},\n", + "$$\n", + "\n", + "Where $\\epsilon$ is the volume fraction of the conducting phase, the porosity of the electrode for diffusion within the electrolyte, and $\\tau$ is the tortuosity factor. A measure of the effect of the increased pathlength that transported species traverse due to the presence of obstacles.\n", + "\n", + "The tortuosity and tortuosity factor are often used interchangably but this can lead to confusion. Tortusosity is a purely geometric concept relating the length of a winding capillary pathway through a medium with the length of that medium, whereas tortuosity factor relates the the ratio of the transport property which may also depend on other factors such as anisotropic obstacles, boundary conditions of flow and also other physical phenomena such as the average pore size which could induce Knudsen effects. \n", + "\n", + "In essence it is a \"fudge-factor\" but many studies have been devoted to its understanding and there are many relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10]. By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." + ] + }, + { + "attachments": { + "c46a76fa-4c2b-46ff-a51a-0498e38e118b.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:c46a76fa-4c2b-46ff-a51a-0498e38e118b.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A recent study by [Usseglio-Viretta et al.](https://iopscience.iop.org/article/10.1149/2.0731814jes) found that Bruggeman and similar relations can significantly underpredict the tortuosity factors. If used at all these relations are often more suitable for the cathode where particles are more spherical but should be used with caution for the anode. A more recent trend is to use numerical methods to calculate tortuosity factors directly from image data gathered for electrodes in which case a straight-forward relation with porosity may not exist and is not necessary if factors can be directly supplied." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remainder of the notebook demonstrates how to use the different options for transport efficiency and supply your own tortuosity factor" ] }, { @@ -117,7 +159,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "856222356e2c448c990304033c81e410", + "model_id": "b7c1d18fcad1404189df6f04015d2a40", "version_major": 2, "version_minor": 0 }, @@ -131,7 +173,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -143,6 +185,13 @@ "pybamm.dynamic_plot(sols,labels=te_opts)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Bruggeman and tortuosity factor results should be identical" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -163,6 +212,13 @@ "np.allclose(sols[0][\"Terminal voltage [V]\"].data, sols[4][\"Terminal voltage [V]\"].data)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now increase the tortuosity factors. N.B this will need to be calculated for specific electrodes with given porosity. Changing porosity in the model will not update the tortuosity factor unless a function is supplied for the parameter." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -195,7 +251,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85ea5ac088354ca6add0e5b83cf0097d", + "model_id": "659e1c020ffb402886bc489053de7811", "version_major": 2, "version_minor": 0 }, @@ -209,7 +265,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -221,6 +277,13 @@ "pybamm.dynamic_plot(sols,labels=te_opts+[\"higher tortuosity factor\"])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The higher tortuosity leads to greater overpotential in the electrolyte and lower terminal voltage" + ] + }, { "cell_type": "code", "execution_count": 12, @@ -239,9 +302,10 @@ "[7] JS Mackie and P Meares. The diffusion of electrolytes in a cation-exchange resin membrane i. theoretical. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 232(1191):498–509, 1955.\n", "[8] 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", "[9] EE Petersen. Diffusion in a pore of varying cross section. AIChE Journal, 4(3):343–345, 1958.\n", - "[10] 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", - "[11] Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random arrays of freely overlapping cylinders with various orientation distributions. The Journal of chemical physics, 98(1):616–626, 1993.\n", - "[12] Harold L Weissberg. Effective diffusion coefficient in porous media. Journal of Applied Physics, 34(9):2636–2639, 1963.\n", + "[10] Lihua Shen and Zhangxin Chen. Critical review of the impact of tortuosity on diffusion. Chemical Engineering Science, 62(14):3748–3755, 2007.\n", + "[11] 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", + "[12] Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random arrays of freely overlapping cylinders with various orientation distributions. The Journal of chemical physics, 98(1):616–626, 1993.\n", + "[13] Harold L Weissberg. Effective diffusion coefficient in porous media. Journal of Applied Physics, 34(9):2636–2639, 1963.\n", "\n" ] } @@ -249,13 +313,6 @@ "source": [ "pybamm.print_citations()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index 885325c271..b8073bd89d 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -774,3 +774,13 @@ @article{mackie1955diffusion year={1955}, publisher={The Royal Society London} } +@article{shen2007critical, + title={Critical review of the impact of tortuosity on diffusion}, + author={Shen, Lihua and Chen, Zhangxin}, + journal={Chemical Engineering Science}, + volume={62}, + number={14}, + pages={3748--3755}, + year={2007}, + publisher={Elsevier} +} diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index dd1ecb42cd..f7864e5e64 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,3 +1,2 @@ from .base_transport_efficiency import BaseModel -from .bruggeman_transport_efficiency import Bruggeman from .general_transport_efficiency import GeneralTransportEfficiency diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py deleted file mode 100644 index 5110ef8d13..0000000000 --- a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py +++ /dev/null @@ -1,46 +0,0 @@ -# -# Class for Bruggemantransport_efficiency -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class Bruggeman(BaseModel): - """Submodel for Bruggeman transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - b_k = self.param.domain_params[domain.split()[0]].b_e - tor_dict[domain] = eps_k**b_k - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] - b_k = self.param.domain_params[domain.split()[0]].b_s - tor_k = eps_k**b_k - tor_dict[domain] = tor_k - - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py index f446cb9643..7db1caf25f 100644 --- a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py @@ -22,6 +22,7 @@ def __init__(self, param, component, options=None): super().__init__(param, component, options=options) def _tortuosity_factor_model(self, eps_k): + pybamm.citations.register("shen2007critical") if self.options["transport efficiency"] == "ordered packing": pybamm.citations.register("akanni1987effective") tor_k = (3 - eps_k)*0.5 From 21670f5e80c1b3b2f9a57b1853738188419fb422 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 11 Oct 2023 17:59:28 +0100 Subject: [PATCH 10/35] Update docs --- .../transport_efficiency/bruggeman_transport_efficiency.rst | 5 ----- .../api/models/submodels/transport_efficiency/index.rst | 2 +- docs/source/examples/index.rst | 2 +- .../transport_efficiency/general_transport_efficiency.py | 2 +- 4 files changed, 3 insertions(+), 8 deletions(-) delete mode 100644 docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst diff --git a/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst b/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst deleted file mode 100644 index f5e5f1c1bc..0000000000 --- a/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst +++ /dev/null @@ -1,5 +0,0 @@ -Bruggeman Model -=============== - -.. autoclass:: pybamm.transport_efficiency.Bruggeman - :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/index.rst b/docs/source/api/models/submodels/transport_efficiency/index.rst index fcdec7077f..8bd8c56905 100644 --- a/docs/source/api/models/submodels/transport_efficiency/index.rst +++ b/docs/source/api/models/submodels/transport_efficiency/index.rst @@ -5,4 +5,4 @@ transport_efficiency :maxdepth: 1 base_transport_efficiency - bruggeman_transport_efficiency + general_transport_efficiency diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index 878a30327e..e03770e9f3 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -70,7 +70,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/submodel_cracking_DFN_or_SPM.ipynb notebooks/models/loss_of_active_materials.ipynb notebooks/models/thermal-models.ipynb - notebooks/models/tortusity_models.ipynb + notebooks/models/tortuosity_models.ipynb notebooks/models/unsteady-heat-equation.ipynb notebooks/models/using-model-options_thermal-example.ipynb notebooks/models/using-submodels.ipynb diff --git a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py index 7db1caf25f..ef0f0e3e18 100644 --- a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py @@ -6,7 +6,7 @@ class GeneralTransportEfficiency(BaseModel): - """Submodel for Bruggeman transport_efficiency + """Submodel for transport_efficiency Parameters ---------- From 34d306828677e8ce30c862892fad14513006f612 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 11 Oct 2023 18:26:13 +0100 Subject: [PATCH 11/35] Forgot the new file --- .../transport_efficiency/general_transport_efficiency.rst | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst diff --git a/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst b/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst new file mode 100644 index 0000000000..913d3a6a8e --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst @@ -0,0 +1,5 @@ +General Transport Efficiency Model +=============== + +.. autoclass:: pybamm.transport_efficiency.GeneralTransportEfficiency + :members: From 488d6e4d83a063268b6dfcdecc3428dea93b354d Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 11 Oct 2023 20:05:46 +0100 Subject: [PATCH 12/35] Update title underline and add well_posed tests --- .../general_transport_efficiency.rst | 2 +- .../base_lithium_ion_tests.py | 25 +++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst b/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst index 913d3a6a8e..ef22d8868b 100644 --- a/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst +++ b/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst @@ -1,5 +1,5 @@ General Transport Efficiency Model -=============== +================================== .. autoclass:: pybamm.transport_efficiency.GeneralTransportEfficiency :members: 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 6815698588..b8a9ddbf8d 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 @@ -389,3 +389,28 @@ def test_well_posed_current_sigmoid_diffusivity(self): def test_well_posed_psd(self): options = {"particle size": "distribution", "surface form": "algebraic"} self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_Bruggeman(self): + options = {"transport efficiency": "Bruggeman"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_ordered_packing(self): + options = {"transport efficiency": "ordered packing"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_overlapping_spheres(self): + options = {"transport efficiency": "overlapping spheres"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_random_overlapping_cylinders(self): + options = {"transport efficiency": "random overlapping cylinders"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_heterogeneous_catalyst(self): + options = {"transport efficiency": "heterogeneous catalyst"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_cation_exchange_membrane(self): + options = {"transport efficiency": "cation-exchange membrane"} + self.check_well_posedness(options) + From 303945044f8d4d0020ceffde6021d8eeda2ebf80 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 11 Oct 2023 19:07:00 +0000 Subject: [PATCH 13/35] style: pre-commit fixes --- .../test_lithium_ion/base_lithium_ion_tests.py | 1 - 1 file changed, 1 deletion(-) 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 b8a9ddbf8d..937706f684 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 @@ -413,4 +413,3 @@ def test_well_posed_transport_efficiency_heterogeneous_catalyst(self): def test_well_posed_transport_efficiency_cation_exchange_membrane(self): options = {"transport efficiency": "cation-exchange membrane"} self.check_well_posedness(options) - From 197b700e95974b6fd11ca55c448b511bead75ee7 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Thu, 12 Oct 2023 10:03:13 +0100 Subject: [PATCH 14/35] Add some missing tests --- .../test_lithium_ion/base_lithium_ion_tests.py | 8 ++++++++ 1 file changed, 8 insertions(+) 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 b8a9ddbf8d..cc68b67bff 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 @@ -414,3 +414,11 @@ def test_well_posed_transport_efficiency_cation_exchange_membrane(self): options = {"transport efficiency": "cation-exchange membrane"} self.check_well_posedness(options) + def test_well_posed_transport_efficiency_hyperbola(self): + options = {"transport efficiency": "hyperbola of revolution"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_tortuosity_factor(self): + options = {"transport efficiency": "tortuosity factor"} + self.check_well_posedness(options) + From c79113eab344a114773c7db20300917a717f5938 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Thu, 12 Oct 2023 10:47:21 +0100 Subject: [PATCH 15/35] Update the transport efficieny print_name and add a bit more to notebook --- .../examples/notebooks/models/latexify.ipynb | 628 ++++++++++-------- .../notebooks/models/tortuosity_models.ipynb | 21 +- .../base_transport_efficiency.py | 2 +- 3 files changed, 378 insertions(+), 273 deletions(-) diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb index 63e7c0d519..3a899345e5 100644 --- a/docs/source/examples/notebooks/models/latexify.ipynb +++ b/docs/source/examples/notebooks/models/latexify.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -10,7 +9,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -22,6 +20,13 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Invalid requirement: '#'\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -36,7 +41,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -53,7 +57,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -67,43 +70,45 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", "text/latex": [ - "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" + "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{Throughput capacity [A.h]}\\\\\\\\\\frac{d}{d t} Qt_{Ah} = \\frac{\\left|{I}\\right|}{3600}\\\\\\\\Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}^{surf} F L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" ], "text/plain": [ "\\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Dis\n", "charge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.\n", - "0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentratio\n", - "n [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nab\n", - "la\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\r\n", - "ight)\\quad 0 < r < R_{\\mathrm{n}}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}_{\\mathr\n", - "m{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = -\n", - " \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}\n", - "}}\\quad \\text{at } r = R_{\\mathrm{n}}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\\n", - "mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r <\n", - " R_{\\mathrm{p}}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}_{\\mathrm{s,p}} = 0.0\\quad\n", - " \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{\n", - "p}}}{D_{\\mathrm{p}}_{\\mathrm{p}}_\\mathrm{n}(c_\\mathrm{s,n}, T) + U_\\mathrm{p}(\n", - "c_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\\n", - "frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm\n", - "{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \n", - "\\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameter\n", - "s and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s\n", - ",n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\math\n", - "rm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \n", - "\\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}_{\\mathrm{s,p}} = \\text{X-aver\n", - "aged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Posi\n", - "tive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}__{\\mathrm{typ}}\\\\\\\\\\ove\n", - "rline{c}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}__{\n", - "surf} F L__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentra\n", - "tion [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}__{\\mathrm{typ}}\\\\\\\n", - "\\\\overline{c}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{\n", - "c}__{surf} F}\\quad \\text{at } r = R__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]\n", - "}\\\\\\\\V = - U__\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F ne__{\\ma\n", - "thrm{0}}} \\right)}}{F ne__{\\mathrm{max}} = \\text{Maximum concentration in nega\n", - "tive electrode [mol.m-3]}\\\\\\\\\\overline{c}__{\\mathrm{max}} = \\text{Maximum conc\n", - "entration in positive electrode [mol.m-3]}" + "0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{Throughput capacity [A.h]}\\\\\\\\\\frac{d}{d \n", + "t} Qt_{Ah} = \\frac{\\left|{I}\\right|}{3600}\\\\\\\\Qt_{Ah} = 0.0\\quad \\text{at}\\; t\n", + "=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\fra\n", + "c{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\ma\n", + "thrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R\n", + "_{\\mathrm{n}}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}_{\\mathrm{s,n}} = 0.0\\quad \n", + "\\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{ce\n", + "ll}}}{D_{\\mathrm{n}}_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } \n", + "r = R_{\\mathrm{n}}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\na\n", + "bla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}_{\\ma\n", + "thrm{s,p}} = \\int c_{\\mathrm{p}}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\n", + "\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}\n", + "_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}_\\\n", + "mathrm{n}(c_\\mathrm{s,n}, T) + U_\\mathrm{p}(c_\\mathrm{s,p}, T) - \\frac{2.0 R T\n", + "_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\ma\n", + "thrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm{amb}} \\operatorname\n", + "{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathr\n", + "m{p}} j_{\\mathrm{p}}_{\\mathrm{s,n}} = \\text{X-averaged negative particle conce\n", + "ntration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [\n", + "m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}\n", + "}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\n", + "\\\\\\\\D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mat\n", + "hrm{p}}__{\\mathrm{typ}}\\\\\\\\\\overline{c}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\\n", + "; t=0\\\\\\\\\\nabla \\overline{c}__{surf} F L__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-avera\n", + "ged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \n", + "\\overline{c}__{\\mathrm{typ}}\\\\\\\\\\overline{c}__{\\mathrm{init}}\\, dxn\\quad \\text\n", + "{at}\\; t=0\\\\\\\\\\nabla \\overline{c}__{surf} F L__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Vo\n", + "ltage [V]}\\\\\\\\V = - U__\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F\n", + "} - \\frac{2.0 R T__{\\mathrm{0}}} \\right)}}{F}\\\\\\\\\\\\ \\textbf{Parameters and Var\n", + "iables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}__{\\mathrm{max}} = \\\n", + "text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}__{\n", + "\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}" ] }, "execution_count": 3, @@ -116,7 +121,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,9 +134,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" + "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{Throughput capacity [A.h]}, \\ \\frac{d}{d t} Qt_{Ah} = \\frac{\\left|{I}\\right|}{3600}, \\ Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}^{surf} F L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" ], "text/plain": [ "⎡ \n", @@ -149,159 +153,177 @@ "\n", " \n", " \n", - " d \n", - "\\textbf{X-averaged negative particle concentration [mol.m-3]}, ──(\\overline{c}\n", - " dt \n", + " d │I│ \n", + "\\textbf{Throughput capacity [A.h]}, ──(Qt_Ah) = ────, Qt_{Ah} = 0.0\\quad \\text\n", + " dt 3600 \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{s,n}}) = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_\n", + "{at}\\; t=0, \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \n", " \n", "\n", " \n", " \n", + "d \n", + "──(\\overline{c}_{\\mathrm{s,n}}) = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabl\n", + "dt \n", + "\n", + " \n", " \n", - "{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}__{\\mathrm{typ}}, \\ove\n", + " \n", + "a \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}__{\\mat\n", " \n", "\n", " \n", " \n", " \n", - "rline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}__{\\mathrm{init}}\\, dxn\\quad \\tex\n", + "hrm{typ}}, \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}__{\\mathrm{init}}\\\n", " \n", "\n", " \n", " \n", " \n", - "t{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \n", + ", dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\te\n", " \n", "\n", " \n", " \n", " \n", - "\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}\n", + "xt{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}\n", " \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}__\n", + "{D_{\\mathrm{n}}_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R\n", " \n", "\n", " \n", " \n", " \n", - "{surf} F L__{\\mathrm{typ}}, \\\\ \\textbf{X-averaged positive particle concentrat\n", + "_{\\mathrm{n}}__{surf} F L__{\\mathrm{typ}}, \\\\ \\textbf{X-averaged positive part\n", " \n", "\n", " \n", " \n", - " d \n", - "ion [mol.m-3]}, ──(\\overline{c}_{\\mathrm{s,p}}) = \\nabla\\cdot \\left(D_{\\mathrm\n", - " dt \n", + " d \n", + "icle concentration [mol.m-3]}, ──(\\overline{c}_{\\mathrm{s,p}}) = \\nabla\\cdot \\\n", + " dt \n", "\n", " \n", " \n", " \n", - "{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\m\n", + "left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\qua\n", " \n", "\n", " \n", " \n", " \n", - "athrm{p}}__{\\mathrm{typ}}, \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}__\n", + "d 0 < r < R_{\\mathrm{p}}__{\\mathrm{typ}}, \\overline{c}_{\\mathrm{s,p}} = \\int c\n", " \n", "\n", " \n", " \n", " \n", - "{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,p}}\n", + "_{\\mathrm{p}}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}\n", " \n", "\n", " \n", " \n", " \n", - " = 0.0\\quad \\text{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_\n", + "_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,p\n", " \n", "\n", " \n", " \n", " \n", - "{\\mathrm{p}}}{D_{\\mathrm{p}}_{\\mathrm{p}}__{surf} F}\\quad \\text{at } r = R__{\n", + "}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}_{\\mathrm{p}} \\overline{a}_{\\mathr\n", " \n", "\n", " \n", " \n", " \n", - "\\mathrm{typ}}, \\\\ \\textbf{Voltage [V]}, V = -U_\\mathrm{n}(c_\\mathrm{s,n}, T)__\n", + "m{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}__{surf} F L__{\\mathrm{typ}}, \\\\ \\te\n", " \n", "\n", " \n", - " 2.0⋅R⋅T_{\\mat\n", " \n", - "\\mathrm{surf} + U_\\mathrm{p}(c_\\mathrm{s,p}, T)__\\mathrm{surf} + ─────────────\n", + " \n", + "xtbf{Voltage [V]}, V = -U_\\mathrm{n}(c_\\mathrm{s,n}, T)__\\mathrm{surf} + U_\\ma\n", " \n", "\n", - " ⎛ 0.5⋅j_{\\mathrm{p}} ⎞ ⎛ \n", - "hrm{amb}}⋅asinh⎜────────────────────────────⎟ 2.0⋅R⋅T_{\\mathrm{amb}}⋅asinh⎜─\n", - " ⎝j_{\\mathrm{p}}__{\\mathrm{0}}⎠ ⎝L\n", - "───────────────────────────────────────────── - ──────────────────────────────\n", - " F⋅ne_{\\mathrm{p}} \n", + " ⎛ \n", + " 2.0⋅R⋅T_{\\mathrm{amb}}⋅asinh⎜─────\n", + " ⎝L_{\\m\n", + "thrm{p}(c_\\mathrm{s,p}, T)__\\mathrm{surf} - ──────────────────────────────────\n", + " \n", + "\n", + " 0.5⋅i_{\\mathrm{cell}} ⎞ \n", + "────────────────────────────────────────────────────────────────⎟ 2.0⋅R⋅T_{\\\n", + "athrm{n}}⋅\\overline{a}_{\\mathrm{n}}⋅j_{\\mathrm{n}}__{\\mathrm{0}}⎠ \n", + "───────────────────────────────────────────────────────────────── - ──────────\n", + " F \n", "\n", - " 0.5⋅i_{\\mathrm{cell}} ⎞ \n", - "────────────────────────────────────────────────────────────────────⎟ \n", - "_{\\mathrm{n}}⋅\\overline{a}_{\\mathrm{n}}⋅j_{\\mathrm{n}}__{\\mathrm{0}}⎠ \n", - "─────────────────────────────────────────────────────────────────────, \\\\ \\tex\n", - " F⋅ne_{\\mathrm{n}} \n", + " ⎛ 0.5⋅i_{\\mathrm{cell}} \n", + "mathrm{amb}}⋅asinh⎜───────────────────────────────────────────────────────────\n", + " ⎝L_{\\mathrm{p}}⋅\\overline{a}_{\\mathrm{p}}⋅j_{\\mathrm{p}}__{\\\n", + "──────────────────────────────────────────────────────────────────────────────\n", + " F \n", + "\n", + " ⎞ \n", + "──────────⎟ \n", + "mathrm{0}}⎠ \n", + "───────────, \\\\ \\textbf{Parameters and Variables}, I = \\text{Current function \n", + " \n", "\n", " \n", " \n", " \n", - "tbf{Parameters and Variables}, I = \\text{Current function [A]}, \\overline{c}_{\n", + "[A]}, \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concent\n", " \n", "\n", " \n", " \n", " \n", - "\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, D\n", + "ration [mol.m-3]}, D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s\n", " \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, T_{\\mathrm{amb\n", + "-1]}, T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, c_{\\mathrm{n}}__{\\mat\n", " \n", "\n", " \n", " \n", " \n", - "}} = \\text{Ambient temperature [K]}, c_{\\mathrm{n}}__{\\mathrm{max}} = \\text{Ma\n", + "hrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ove\n", " \n", "\n", " \n", " \n", " \n", - "ximum concentration in negative electrode [mol.m-3]}, \\overline{c}_{\\mathrm{s,\n", + "rline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mo\n", " \n", "\n", " \n", " \n", " \n", - "p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, D_{\\mathrm{\n", + "l.m-3]}, D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}, c_{\\\n", " \n", "\n", " \n", " \n", " \n", - "p}} = \\text{Positive electrode diffusivity [m2.s-1]}, c_{\\mathrm{p}}__{\\mathrm\n", + "mathrm{p}}__{\\mathrm{max}} = \\text{Maximum concentration in positive electrode\n", " \n", "\n", - " ⎤\n", - " ⎥\n", - " ⎥\n", - "{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}⎥\n", - " ⎦" + " ⎤\n", + " ⎥\n", + " ⎥\n", + " [mol.m-3]}⎥\n", + " ⎦" ] }, "execution_count": 4, @@ -314,7 +336,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -331,7 +352,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -348,7 +368,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -362,7 +381,7 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", "text/latex": [ "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model with electrolyte Equations}}}$" ], @@ -375,7 +394,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\\\ \\textbf{Discharge capacity [A.h]}$" ], @@ -388,7 +407,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIUAAAArCAYAAABWzEuiAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAIHUlEQVR4Ae2b0ZEUNxBAh6sLAOMIDBkAjgDIwJgIgAyg+OPvCjIwF8EBGYAjOCADcAQ+yAC/p1NPaWZnd2bvZnZddeoqrTStVkvqbrVamtlrP3/+bKbCy5cvb0N7TLpJesvz06ltK928EkD2r+CoPu5nzh/Jf5AeU2d+Ybi2jVFEL3SqJT0kfx+4mu9HAlkX38hvzTWCg20Z0Xlpmds2r/QzSgBd6CmEWRfn1kbBAB6QtMxLuShnUuHSEogF+uHSnAoGFzEKB+L+VWH/EnCBNizQWfVxuGledGZAaUDzjfQvyc51WUekCvuXgAv0y9zDWGsUGIQdviPdoaxRNOThpma1THlX2E4CWT82Otmu5Tj14PZBh9dpqkE8p5wMIrOy/AVcjSeyQPaYpa2D/mdfoINGQUduGddR/pvepPUesw+i10d9nCYBddGgo9m3j3VG8Sf9dZRP53oPY4zYQihW2KMEjO06OpprLCtGkZWvAfSVr6FomYsMZK4JXQU+6CB5Ceba19Es018xioJrGUuIdg9LropB/UHSa1TYjwQWiyeczopRoGyDSL1Bq3SNgGfd1SeS8ABc32jOa+rvLiSgPhp0MHs8Id/Bdx905vZxTDol/Ury2KOxGICK+7jUgOBdYUAChU7UTWwfXm97u/x8oMmFUYNGcWFuIw0ZvN7HN6tOTCMTxH2grn/SSZX1Z/cS2JlRoHS9jBbuq92O2+PZO5Gb5Hd2L4LaY18CixsFitYr/E06oxwBUn8cDXXfQdZvNFYks3vESqC5wBA0CC/C1hpE7tPg9skC/VeWW0pgUaPAEP5iPJ5aHk4YVzrN0Eb6CnuUwGJGgXINIF35U08qbjPCjfOs/u5LAocLdhzHJL3FFLibifZ6/5G9Wxz5poxbGl8STvGGU/ntlW5JowjBjl6LI1C9hNvGD8qjRiE9KY60swoQvlf+Y+TFtg805fahkqcoL71XgT68y1pFaxBU/kO+EnuAe0L6Snq2lkGtGJXAIQKc/o3/CDt4XStIRle8tLRRyd5h6IKnXGC9gNY2Gl3/vuMNPOQ16p2g2TkwttlkveTgNYpSkXP2pcLSHX0wpS+3FF1/+fWxShRG9+Tc/gRaA1iNogPUJxx5x1g6RCMPtDUGiq1vhLqtnhRTwHspWbcDmaOw2OUVAtC9fyal/4fwrCJ9oeZ2cp/nWNXi20/+KK8F2jwjvSbJ1zv/jiHxLC/vQ+KVsvV+PXZhI6H9lYODpWacFWGMcEzZFayHcEW5rdwiT1fblH/LOIrrAZpkEJlC40peoddCg/C6XINzK9I43G4qbCGBJU8fDYpxVbu/u0VoFL5xFdxWnua6hKB8mzS4osHrzss44SvPEZym9vlHunsFwn9NTYptijZXvrioUSjdrOi+m++cHKDxtFDGGX3F2N6TzKNcEZ5HQ3M7asiT5yAvDUsjGT3RZJ41yxLYaBQIWOX5XYUCn/tllfcB3nbqNR6RvyZfAfBuG527A54dj+3Mwwg0ALeVBJnGreQ9aa0XCvpd5nlsMac4Sb0CX3rDdkjgIxgP3Am4mHfCFTT+P0cPKb8VLzmFbswo7Ngg0KNUBG8U08p0MgZ8F/kKy8F6pxBHtMFX5tSr6OQJyFsAb5Dpc2kUZYBpnW3D++hhOkKUYB/AuJWbwW8YRUNZA/ebks6ftnl2fsZe0ieDIffZ1P6hGJx6OCJP8yVPuiHv6GYq3QHMNgKMFK7Qt2LxDvrMyi3BY5+GocIVxIrCwEnj5DtvV8G7ZVgnvKAc41MQYQTWhRBdZUci/ifgCekJ49YQAkK2/aDY+esVol5659l6AOrkp0zauVNWrj6HnJqpdLRpDv0ZAZUy9IfidfgRdm2c0Vr6UAMm4UpqV1PQ5Amv1IHvG4+C68QywWPPuQtApZkSOCdSfmpxGo3b972yoj9P6pzjyqICd0py6424ayrdJKNwJZaWymMC8a11ZlzNRiSAkpTlLyUZuPAa7cqmXsPXWFrjKdsUZfUwdBMc3iT0NJWuaxQMwO1AdytDAxYnoLUekRrqZexgr5OkNYDTxZ2SDwaK1FXYIIEsU2XuEb1U7l1wemjlb0wUAeQ7cGmRkquHMbgxlS4YHUaBhipcBbe3i+AiuIxBmMeJ4T71HZcdvGo+LgFkp7KV+e8k3f8nUgmh8LvQtsdqyt9Jfueql76RG2zyJvKZSpfYJaOgAxtqEEa54XYksOwtZL9TjWFoH7NNhQkSQKbKL8mQstvHZ/J4JaA+BD1x6T3EvSV5S5wWqogRiAvDEbL0V45EE6cP3ZcBSX8AWvJQ5+vwYx3X+gEJIHdXvQvPrSEMQspygfosePyUxu3ljLQOwju47UylS7zCKLwy7ig/D864IbaQ1GAdPlXWn1EJID9Xv1tHH2L7cFvWQITIz5+6v17MRb1G0ofAlSfHwJW0gWsN8ADGIk0d5fOc3i1Q3zEW8HqJpsRnHqIrjEvAle5WEcpY10K5b6IJJUrn4u1DeIrQ31S6zn9Jo5Ng3sYNTKD8Q3GLl9A6sqFBBZ+adyXwg0ev3s1LcDsQQokeT4fk6u2vR9WgMxaMthRbkK6MB6fSNXoKB9exoqxoXVy4tPK6VAtMBgSdlqwbSwET5QrjEvAk0fHKWd7K0mNpMhZy4wxPesZ7CShLowd/fI5Ji9I48Iw6F2eCy9DJIH1kk5kc83xKMlo9ITk4BySu/UwfWq1XK9byGp77wanoChskgMzcgsvbVmU6+EIM2nQIyOxckL7j6CxCnjUW6dSZgaXH3AvT/QfWxyeB8p2uEQAAAABJRU5ErkJggg==\n", "text/latex": [ "$\\displaystyle \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}$" ], @@ -403,7 +422,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAALcAAAASCAYAAAAZv0/CAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAFaUlEQVRoBe2a3XEURxCADxUBGDsCiwyEHQEiA7AiQGQAxZP0RkEGQASUyUA4AhllYByBVcrg/H3D9LJ32p2d2z3u7KvrqlHPT/dMT093T8/q7szn89kynJ+fH9L3jPID5SaP23fB2Lvc3lnEHl/nzf0Dvk95Td+X2g1P5a9dZ083m5V0fWfZuDPxMYp7Sv2qrUDav9M+BD9o9+9Snb19Zj+vwB/dF1gHt+8R9UEDh2YSv2tuCpBVp32x6nrwrKSTVeevpR/S9UFMpMCZ+Aj8gLJg2NLR9wSkcb8Nvl3C7OuU/aiHZNjujbo3l+3BPU/ld71NAbJ6E2ukY8DgJ//1GOZ18NToujFuFvyD4sE+Glj8E+MawS6CznvLqem7pByjmyFjmMq/SZ0+nrCYNvIFfej424JBXSfjRkij0hFFhiFIVzM80u8aGJG6olGkI46XYCp/ae61jeWzi3fFmHndZ3O7jZlgDTyDur7LRr1ejMSfqHdFrWU5Inr9uDzwf26z99hXaRu9e57KX1p0lTHkSKlV5vkpY98QKcqCjdgnud/byHeU8IF6r8EypjHFRwZtxvRV3kvwG/DGgPWqzuouEsWDYjCnzNL/knFEs95NKQQlKbWXaMQAcyqryl4FruAr3UxhuCV5S0qdyr/KXjpp2Z+Ga0T2zRQ3rLoy5UwfAejXgD+C/wIb0DTYQYDOdFR619AphtLXNCd0WzsrjTuMROGLgKAe7hHlhnrRuDPt3+CHlIUbgbbRRad6S31lr4en6kCYf90QkXDsvFP5x6xrdD1FZ34IKJ5Z5eQa9cJ5lvi2eVYat1eMxlqKWCH/b7kS0T76u/BLOnUG519QBmu9oxhhBh0Kmk3BdWGhiMp+9+6Dqfx981b3o9MUlWWgru69ZasirDyVYDDsTV8q55hKVqXrA1bRm0vESZCsLA3S6734jxzGVcAHig6jcS8A46kPfLUwsMUGsoRzaxTLEH29kW8q//KCY9vIYSpoKvCeotx+6VkLODcTeXYXa5lw5CS1ujZya2DmUQ3ArHGqpLaHathCKW/9SvH1sfEGfo3h1+hsYec3f4tPis75gnaVsUP3PfI4xfMmueWM9EXkHrpppvIrw2hAL56jRv2EepIVbBo5CNDV/EPHc5vF3Lmundz0LcDY1s5K435FeYwQFh8aGtyfFL9jmqtFCmFK0jxUqHcC9M8pkUc7j1fjMnhVmgOmGwBsRDCNqXGcGfTfK+c2Pw0nptqAjzFvrN5DzJRT+ZsFR1Zc3/NqO2E4pnrT0E1BDTpDe+kSYSHfZh6dybl6gxI0WzurAxZXMHPo99SNWnqiB6nQ98EqzP6fcx/VbmA8ReTWqC/yrkgo3dMWnb/fcL2tAvLrbNfg5iajruPp2I289lHmFP/V3gDtKv6GYTOViNzuo30WOkBX4ClJpaOkc1IHzgfuNezSRFPHanTd/LYEYpVg9FToENhDfsZYEwmko8Q4w9+Afq+gm289SZnOcY+x1A9WwZ/B94KOuk5gWtJOg2J4oxgZ3L/RW3l9QJpW+Z14Yc9Z5luf0mr5mXPtwNpxhpdMrhGqax3Oc/UcFr5OQW/gEqRtvoWnno4/0DufZ5z4aKebt4N0I12sXz4rfzjVV87Ozi4oxzFO/TnlMNpt7Fi7bV1aypxyFGPUTykXrXaiyfQNXYzvcf/57HVT1s1BhYulnAkv0fNPwLfSB/pMM1Jkbs/XotXjA8zb2q9teSNinwTRHu81MFUDQ8atIfvQ9EffXkVN3hkLMxbXlEbbAP3mpY4JL6lrxIJXSRiz7XjVmwr4uN3DXgNr0UCTc3fNhkGaw2nUPiT8fXfbKLtY9n17DfxnNPAvcplXrUqqBf8AAAAASUVORK5CYII=", + "image/png": "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\n", "text/latex": [ "$\\displaystyle Q_{Ah} = 0.0\\quad \\text{at}\\; t=0$" ], @@ -416,7 +435,48 @@ }, { "data": { - "image/png": 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QEGgINARWIxA3zPRFa7upWA3lYkNhy0vm/njjsr2TsghZq9AQaAg0BBoCtxwB3idMXzjeciz27j5fIfmrwIv/AyudsADQ0wIjAAAAAElFTkSuQmCC", + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\\\ \\textbf{Throughput capacity [A.h]}$" + ], + "text/plain": [ + "\\\\ \\textbf{Throughput capacity [A.h]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\frac{d}{d t} Qt_{Ah} = \\frac{\\left|{I}\\right|}{3600}$" + ], + "text/plain": [ + "d │I│ \n", + "──(Qt_Ah) = ────\n", + "dt 3600" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL4AAAASCAYAAADlqmQIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAFsElEQVRoBe2a73UUNxDADz8XEKCCmA5MUkFMBxAqwHQAj0/2Nx50AFTACx2YVODgDkIqiJ87cH4/ZUdPt7e3q7298z9u3pNHGs2MpNFoNFrfvcvLy1kbjo+P96C9pPxEuWj6pZ3Q97Fp/7AIG7xrFv8v+BHlHbTvtQaZKl87zo/O12fne23Hb5gPMNoL6mel8Wj/QXsP/LikW4fmIflGeUK92gmUvU3A2lzjW/AX5w0ete6p8ldlK+bpYX49dryx9hirv5Z/yM47ocgJN8z74MeUOaeXD9ozkI7/IeQK7GHxVjgvaHeqyroPWZB2Sk7v4qh7I9rusoksGabKZ0UbrjBP99EDvQpcux/U2Dk7Piv8k+KmPhlY7Vf6dYA2KPcdeR3hroIHfyEgQDulHLD2IWeZKn9Vdn06YaCb4AeDdk6Oz4YZrfYpCgxBSmOQkb8ET3qOhGXHHaq7xq4bLVI7+/tgqnyf7rX0Nfsab5hVdN4EPxi08y4L9Vozgn+l3hXN2ouPqPYAfgeIR7B6TJN8B5yC37cFb3Ob9cS6+5bxYFnnVPllesfQmUNK1RqZhw32vZJuabCR/nlD9wZzL4XP1JcGNfpujB8wl6p92mVR8YAZzFGTCWazXxpsWmOk88BoMA01lCY1orMZvKZVyeCZuKYKel2LmzEGzpDru/HCqfvm3Gf0qfJj1rLAy9rcIyO577e4tbWTKW76WAFd5/4C/hvsvhrUBgE+099V/WDde1VlZx0/HMTJ9wILdGP3KRfU43pXRoevuS3knTV6/gH/RpmTo21U8jB+oL7SrYFc1YY5lzVDRNFV1U6VHzuuEf0Qe/nBotzPsXqCf5QfKHRNe/VQxzdF0ZH7IplzFH7/H+Vbommmw7P0KgymAr+h7iFy7Lbjf2QuRqbBgwjPVcJ5z2ARZfyuvwymyi/TW0XHpimay0xd23tzV9/QylWAQXSMH1SoHM1SZecd1HrS+5jTyI2xdEhTgvxPrIauA58kxoE/8GuczxQPmnJzQH+igc/mOq65wXwiMOg0bQja0qg5Vb494Cpt5mB6aWrxieKc/Rq1FlA3iqr9YC2DdiiptbMRXwcz/8uAsM6pkcrTq9ML7TxY3hm8OUJTV/ZCegf4AH5P0Ul+7ehXn/lifDJ1vNe0qw8CvOvOG2OarnHhsEKLiJ9tEAItPFW+pa6+iU3cYx3+GfU0T7Bp6yDAV/PPrLF+kMZF9yb2atDOu4z+lvKUCVh82Ohwf1F8vJr/RephmpMfRtQD5vI6+DWwTr3gqPS9okTe7hhet21QnzlnulXARhJTo/aBa8vlNjKbyvHNiSMA5PGo+Dj0Jlx22IN3qnzoWQU7tntZHs44sDPoHgJTXvduaB1d41f7QSnMeJvYq0E77zCwDupj8hN1o5nR2k3UAI/AKpH+c0OjOgcaT94Z/TqpTtvl9CmSy9eAXw66oqd8L4IJ7G9hkv6Cdi1V1uVhPAfnG7JZs0Ehz1ka5ZLizxsy0K6SzwKbr0TET/tWDOfh6ApKBctCtcoPFqQ2QKixc/6tDswawciqEcJx3eCX9OUoIR8l+mfUdV6vKw+I7Zz/2w6ALk8ZSZRT/336Eh0s7Rv4PjgBdQ+IqU6ZdjW9V4+Yh/Yx6jtnH7Oma34LzzahPaPd+UmwVl4d6wTGjf09Ra+BRFu7V+65+zD3FQ3+tJ/Q5c3f+ql3AvxVftApvAHioJ39kdqycnR0dEI5iH7qryh70a7FyrV51UO5pOxHH/VDyknRTjy2S77o3+Lle7e1Tb9tdioOW8rBOEFGhefgUWkH/KYuZaRPQxZ6jBQB5onl1yFlI9LHfxSDd4u3FljZAkOOr5P76PVH+159OY+tGRG5SIF06AzQzYHtE95Q18EF04hwdNspxaLf1MJH+Ba2FliLBXKO36UNhzMv1OF9uPj7/NIpu0S2tK0FboUF/gMeGZ9opKKRfwAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$\\displaystyle Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0$" + ], + "text/plain": [ + "Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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QEGgINARWIxA3zPRFa7upWA3lYkNhy0vm/njjsr2TsghZq9AQaAg0BBoCtxwB3idMXzjeciz27j5fIfmrwIv/AyudsADQ0wIjAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}$" ], @@ -429,7 +489,7 @@ }, { "data": { - "image/png": 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6Bj0Y8jQ/GvECG892BBwBR8AR2AcBPGyOmvS3OgJPlfFSEcOc+0r6zv51rIun7uQIOAKOgCOwAgJ42BBedHeUjwwZYE6FcJ6Qv7XJ/YALnnnIV3nYVlFc2gNXVSdHwBFwBByBfRBIj/XhTT9SSL9iziHvkveNN82+Nr+St1G97rgf106OgCPgCDgCbRH4Dz+2hkkLf2m4AAAAAElFTkSuQmCC", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}$" ], @@ -452,7 +512,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0$" ], @@ -466,7 +526,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0$" ], @@ -479,7 +539,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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1xjaxgwZ+tfJKQzPH1e6slqDb2FSSrMjPG43SaIcTwWc5CO22DbK9dW6LxO85SiCO8zf41LVI0S45VtpxPsW9R6RDLOTagrbMNf5oHPT9Uq42tvbb8M4QivKmUcLQkg3pbgJ5r7yrdiWttE6++LEh81ev0GTvyaEpo4lyvYm0zkuvCXWqWNC5SSBA9AafuBbllK+DrtNDwOvGKIHNtce2b/kpAx9o49r9ntJu8JyeBC5Ob0jnMyIBAWDJcXajsGlO8nmD/73cM4Xd1grLOdMcm8ocdZ7zMzrasQ/+o7qjHVV0/CwloE0DYMOMgH2Tt/X+dyPZaDCLYEoo7ZpKGqY56hxuMXKPTQL/A3yY1PEhc25PAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}$" ], @@ -494,7 +554,7 @@ }, { "data": { - "image/png": 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PVEcQwsNrGm5PGjQNNA00DTQNNA00DUgD7TXNym6gQMPvgXlvxu0H78v83mzwndnKIsySk4yD741V372DnCXSECY10HQ8qZ7W2DRwLTWgecuBsffNyLUU9BYJZZ23YOQWGbUNpWmgaaBpoGlguQbixtj9+k/ldkBbrs7JntItH5/7g/Dz9s3IpLpaY9NA00DTQNPAHdIA3/V1v466Q+M+xVD51Y9/hffh/8t9YNBuNsUnAAAAAElFTkSuQmCC", + "image/png": 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PVEcQwsNrGm5PGjQNNA00DTQNNA00DUgD7TXNym6gQMPvgXlvxu0H78v83mzwndnKIsySk4yD741V372DnCXSECY10HQ8qZ7W2DRwLTWgecuBsffNyLUU9BYJZZ23YOQWGbUNpWmgaaBpoGlguQbixtj9+k/ldkBbrs7JntItH5/7g/Dz9s3IpLpaY9NA00DTQNPAHdIA3/V1v466Q+M+xVD51Y9/hffh/8t9YNBuNsUnAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}$" ], @@ -507,7 +567,7 @@ }, { "data": { - "image/png": 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uX/f680tHwBFwBDaLgGwk28bVv69iAz5SB7YP3X+pF+qoHKMMcSypoVa7xb8B0DD1hCPgCDgCjkAWAbZEbsXSt5laeN/8mlffoAdD3s6PRjzDxrMdAUfAEXAEliCAh40L39/qCDxVxktFDHPKxe/sX8e6bJ04OQKOgCPgCKyAAB42hBfdHOUjQwaYN50vFPhbm3Ccj/wW4ZnbMb+wraJ6uT3wVjNPOgKOgCPgCMxBoH2sD2/6VIGvehtl/xBAxhlvmn1tzl/vdN0c9+PayRFwBBwBR6AuAv8BAIm8inwXD9QAAAAASUVORK5CYII=", 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uX/f680tHwBFwBDaLgGwk28bVv69iAz5SB7YP3X+pF+qoHKMMcSypoVa7xb8B0DD1hCPgCDgCjkAWAbZEbsXSt5laeN/8mlffoAdD3s6PRjzDxrMdAUfAEXAEliCAh40L39/qCDxVxktFDHPKxe/sX8e6bJ04OQKOgCPgCKyAAB42hBfdHOUjQwaYN50vFPhbm3Ccj/wW4ZnbMb+wraJ6uT3wVjNPOgKOgCPgCMxBoH2sD2/6VIGvehtl/xBAxhlvmn1tzl/vdN0c9+PayRFwBBwBR6AuAv8BAIm8inwXD9QAAAAASUVORK5CYII=\n", "text/latex": [ "$\\displaystyle \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}$" ], @@ -530,7 +590,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0$" ], @@ -544,7 +604,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0$" ], @@ -557,7 +617,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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6/lA/X8AdxafJ1hho91oOvz6u+hvXYqMZDwAqgFRNNqTZsQH/R3pdZFRNpEwCPIW5HMuv56BBcm6KlooGyBUY0vq8pKnzVDWnc5KAA+AOT1sLbaob6rwBxQUgiAv4ID/6Gvuw07im4FF4LJdKSxtCSxtD+l7hWWjTkxYugYuFj++Uhrf4H30d8DDChmDlJ9oQ5uBpXXT/RCXgGuBxPLi1Fnn9Pttx9OxwvZj8t980lDl4Hk5C3vLOEnAA3FmEkzDAFHP6XwJzbAhz8Py/xx46SQm4CXzAxyatj0P9N3RBYd6GOm0kMMeGMAdPf14nLoF7/Ps7J5fAMUggbghsBFxpsTuAO3VtDp47dcgrH5UE/gMXdJiPZc/TCQAAAABJRU5ErkJggg==\n", "text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,p}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}^{surf} F L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}$" ], @@ -572,7 +632,7 @@ }, { "data": { - "image/png": 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", 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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Porosity times concentration [mol.m-3](Negative electrode porosity times concentration [mol.m-3], Separator porosity times concentration [mol.m-3], Positive electrode porosity times concentration [mol.m-3])}$" ], @@ -587,26 +647,22 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\frac{\\partial}{\\partial t} (\\epsilon c)_{\\mathrm{e}} = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\epsilon^{b_e} \\left(\\nabla c_{\\mathrm{e}}\\right)\\right) + \\frac{- \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases} t_{\\mathrm{+}} + aj}{F}\\quad 0 < x < L_x$" + "$\\displaystyle \\frac{\\partial}{\\partial t} (\\epsilon c)_{\\mathrm{e}} = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\mathcal{B} \\left(\\nabla c_{\\mathrm{e}}\\right) + \\frac{i_{\\mathrm{e}} t_{\\mathrm{+}}}{F}\\right) + \\frac{aj}{F}\\quad 0 < x < L_x$" ], "text/plain": [ "d \n", - "──((\\epsilon c)_{\\mathrm{e}}) = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\epsilon_\n", + "──((\\epsilon c)_{\\mathrm{e}}) = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\mathcal{\n", "dt \n", "\n", " \n", - "e} \\left(\\nabla c_{\\mathrm{e}}\\right)\\right) + \\frac{- \\begin{cases}a_{\\mathrm\n", - " \n", - "\n", - " \n", - "{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases} t_{\\mathrm{\n", + "B} \\left(\\nabla c_{\\mathrm{e}}\\right) + \\frac{i_{\\mathrm{e}} t_{\\mathrm{+}}}{F\n", " \n", "\n", - " \n", - "+}} + aj}{F}\\quad 0 < x < L_x__{b\n", - " " + " \n", + "}\\right) + \\frac{aj}{F}\\quad 0 < x < L_x\n", + " " ] }, "metadata": {}, @@ -614,7 +670,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\epsilon^{\\mathrm{init}} c_{\\mathrm{e}}^{\\mathrm{init}}\\quad \\text{at}\\; t=0$" ], @@ -628,7 +684,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHMAAAAWCAYAAADzeqMPAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAFZUlEQVRoBe2Z/XEVNxDAzx4XAE4FkA4MHWA6AFJB7A7C8J//Y6ADSAfBHQAV8NFBSAUxrwPn95O1h05P59zznZl5hp3Rk3a1Wq32S/J55/z8vPsJ12eBk5OTW0i/GzuAf47x3L6WvQfhD4S+oK0q4SrxlPmXJR38FfhRSWvxVfMDFBl/Q9inuccp+OMBw81CDjnOm+JIO5x3EZtnmfpOWO1EZrLBIwix6Tvwh4ll5If5r0zp7NcjLKNk1hipBoUHbToTHpX8l34QTNC2CtBfu/5Jf7tWPM/NtnnssRsbQDhlHNl5CN6XhuCJnjmd0NFv7Mi87gu97TI4uGzyJsxhv0Vt3jszG+d5YaSnxbgeOlfy1vOzcA5p+U0BM0vQdiwu7TjL5rUzy0w7ykYdmASaGauhS94Bzxwk7/l+jowtW1vacZbN98qDY8gVzdS3zgs+dOo76xia91yUZPkSQPNi9671cSOc0d5ALxVOE62fvD4udFleQAv8JeM+cjPvb4Ucs/kdzXt8TTf5oHueezQD0jIf91XIPYanL/+M3TsqhPKdU/6SL9JZNkefHgbOzFTTPpyp42pnapAHmTd1HM6DfqJppHtxWPr0qKLXSBrxUoDHvXSar11lrb2mFcC8BtbQGiI9LDLtLbQnjO/QeocyVr9/aIK66xTPIb9jA9A91df9y/M8BDdIOnqd/4n+Mc2gXwo2tnlr492aiJJGXUTeXfCIzI6xBviSecqlGlbja4hYK78H1hAHjCPDQGeDxh4A8t3HvZ17NpjkNZnpf8H3mWYQRJCqt2AQBk2nSX8NLTlSBsbxJ9SSZ1HuVWyuSgNYc2ae9c+GgChB4o6Noh5QROPpZOHDRTf4NfoFS/AiwJ4GiboMKgR4lMj6JRwOM/tKWGXEYEtBmM8TAWy1qUE+g3wtoGrGDfHJNh+T2yqzHYoakUafCh8y1hi2fcZ1eQlDMd2EMFjH2lu0Hm9yTyQiJ5VDeg2v837NvRLiznYsfKQFTyLkn3BIcmSm3S8YvB7qv7fjvO6xyFncj302sXmh4rdh05l52kdLZJNZoPKth0xtuLy878JgEhYxAAfXoAabd5xl0KhWN2lRJRj2oP5PbKyVN+5MGTRiZLT4mT8ZnjNXB2/MXUc/1ebNvS9zpuU0nBkGWvuKAY9RH9By7C8xWRktyJN61n6l3aYZHJY/e7NTRyVgnEdrnXqppyU/7rv08GFNfye6Ctw71aEQWXiBXf/vVJs3NRl1Jgeqn8xjf47Ip0EOaa0X60HeuZXVTaUgRvlKgYB8HRcZYxkUF8p7RnzM+OrmnFkYjxz5x0BdDWBL7IA/62Iw+cotMxrSPEDeJJuP7bI7NpHppbEGD59qna+8VLpQSMMlYKxBxI324wvqpN/IlggES2TQykoQ8x3yHe9n6eHs2Mw1OtPsPi+a+FtaVJ7ED66u3qO+F6I6dYyV4UvXb9eLOhKZAVNtHvx9339o7ynVAKXTi46+lXUDbngsYTovjOmBNVYd3Wv/d4NnpxQG7qF0ohmpYX+HtqLv6DVq7OUetjPoPlgspRFQ4maj+ngO143B2gd/1ulkA9VqoB7u8wr6Kf0kgNd7vfmhfUwAaybbXBmxx/86c2zDbaFzUB3pBwMd8QA8BYT65zkdZbbJ553cz4PPBuRt7MxNN409djdduIX8OktHWRoHjhKnWb6jhEeZ3sJjdt2Nd2Z2lmX6iHGU395Z0Mwcm2XW7N1a2NtazTdQHCf5qc77z2+93sVmqHegmWi/9LdWRH5/uPF35vc36XBHgses905OAD546AX9Kj2yfGnH382rHyIzr2KoBdd4H9efBJcS76vaK0Q4+w/lbj7tkx0IogAAAABJRU5ErkJggg==", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Voltage [V]}$" ], @@ -641,9 +697,9 @@ }, { "data": { - "image/png": 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", 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\n", "text/latex": [ - "$\\displaystyle V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} + \\frac{0.333333333333333}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\, dxn}\\right) - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\, dxn + \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}}\\, dxn - \\int \\frac{0.5 i_{\\mathrm{cell}} \\left(- L_{x}^{2.0} + L_{\\mathrm{p}}^{2.0} + x_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\epsilon_{\\mathrm{s,n}}^{b_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathrm{s,p}}^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x} + x_{p}\\right)^{2.0}}{L_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathrm{s,p}}^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}\\, dxn}{F}^{\\mathtt{\\text{right}}}$" + "$\\displaystyle V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} + \\frac{0.333333333333333}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\, dxn}\\right) - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F}\\, dxn + \\int \\left(- \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F}\\right)\\, dxn - \\int \\frac{0.5 i_{\\mathrm{cell}} \\left(- L_{x}^{2.0} + L_{\\mathrm{p}}^{2.0} + x_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\left(1.0 - \\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x} + x_{p}\\right)^{2.0}}{L_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}\\, dxn}{F}^{\\mathtt{\\text{right}}}$" ], "text/plain": [ "V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \n", @@ -652,30 +708,31 @@ "n}\\right) - U_\\mathrm{n}(c_\\mathrm{s,n}, T) + U_\\mathrm{p}(c_\\mathrm{s,p}, T) \n", "- \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\m\n", "athrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm\n", - "{n}}}\\, dxn + \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\fr\n", - "ac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}_{\\mathrm{p}}}\\, dxn - \\int \\frac{0.5 i_{\n", - "\\mathrm{cell}} \\left(- L_{x}_{\\mathrm{p}}_{p} \\left(2.0 L_{x} - x_{p}\\right)\\r\n", - "ight)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}_{\\m\n", - "athrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{\n", - "dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\c\n", - "dot 10_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}_{\\mathrm{e}}\\, dxn}\\right) \\right\n", - ")}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n\n", - "}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\epsilon_{\\mathrm{s\n", - ",n}}_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.33\n", - "3333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathr\n", - "m{s,p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0\n", - ".5 \\left(- L_{x} + x_{p}\\right)_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int\n", - " \\epsilon_{\\mathrm{s,p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\lef\n", - "t(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsi\n", - "lon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\fr\n", - "ac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\m\n", - "ax\\left(1.0 \\cdot 10_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}_{\\mathrm{e}}\\, dxn}\n", - "\\right) \\right)}\\, dxn}{F}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)\n", - "__{b__\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F ne__{\\mathrm{0}}\n", - "} \\right)}}{F ne__{2.0} + L__{2.0} + x__{\\mathrm{init}}\\right)__{b__{-15}, \\fr\n", - "ac{(\\epsilon c)__{\\mathrm{init}} \\int c__{b__{b__{2.0}}{L__{b__{\\mathrm{init}}\n", - "\\right)__{b__{-15}, \\frac{(\\epsilon c)__{\\mathrm{init}} \\int c__{\\mathtt{\\text\n", - "{right}}}" + "{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \n", + "\\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}_{\\mathrm{cell}} \\left(- L_{x}_{\\mathr\n", + "m{p}}_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathr\n", + "m{e}} \\int \\left(\\epsilon_{\\mathrm{p}}_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\fr\n", + "ac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mat\n", + "hrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10_{\\mathrm{e,p}}}{\\epsilon_{\\ma\n", + "thrm{p}}_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\ma\n", + "thrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\s\n", + "igma_{\\mathrm{n}} \\int \\left(1.0 - \\epsilon_{\\mathrm{n}}_{\\mathrm{s,n}}}\\, dxn\n", + "}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p\n", + "}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}_{\\mathrm\n", + "{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x}\n", + " + x_{p}\\right)_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\ep\n", + "silon_{\\mathrm{p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\\n", + "mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\\n", + "mathrm{s}}_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dln\n", + "f}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\lef\n", + "t(1.0 \\cdot 10_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}_{\\mathrm{e}}\\, dxn}\\right\n", + ") \\right)}\\, dxn}{F}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__\n", + "\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F}\\, dxn + \\int \\left(- \n", + "\\frac{2.0 R T__{\\mathrm{0}}} \\right)}}{F}\\right)\\, dxn - \\int \\frac{0.5 i__{2.\n", + "0} + L__{2.0} + x__{\\mathrm{init}}\\right)__{b__{-15}, \\frac{(\\epsilon c)__{\\ma\n", + "thrm{init}} \\int c__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{2\n", + ".0}}{L__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{-15}, \\frac{(\n", + "\\epsilon c)__{\\mathrm{init}} \\int c__{\\mathtt{\\text{right}}}" ] }, "metadata": {}, @@ -683,7 +740,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Parameters and Variables}$" ], @@ -696,7 +753,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle I = \\text{Current function [A]}$" ], @@ -709,7 +766,7 @@ }, { "data": { - "image/png": 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AAElFTkSuQmCC", + "image/png": 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AAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}$" ], @@ -723,7 +780,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}$" ], @@ -736,7 +793,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}$" ], @@ -749,7 +806,7 @@ }, { "data": { - "image/png": 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NDHzswVSuDhBiUPE7mtqIsjBAXEin+vf2roGuga6Bm6SBYX0kwLEXRTbI20psHmxeMfhRuUPXQJMG5E+jwKepU0cKGjDd3ZqrD3W0SJCuvjyXVMfvGuga6Br4KjWgdZI30JVyfxrHekniJJAAiDfU/tIoJXToGjikBk4Oyazz6hroGugauCEauNA4udPD6U4O+GRpn5Nzz3tb10DXwEIamH3ik8hxmtR7tWuga6Br4MZrQAEPd7K4q/dUOb/isbsf3P9i3eQ/stvlfFU7dA10DRxKA013fDRBmagvlLigxIVnbkpz8ZZvjVyO4nLT6BKR2jp0DXQNdA10DXQNdA3sUQPaa9l32YsBfqjSA+i1Lib/Skf8KIlT1vCJ+f9I22mI1M9EwgAAAABJRU5ErkJggg==", + "image/png": 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NDHzswVSuDhBiUPE7mtqIsjBAXEin+vf2roGuga6Bm6SBYX0kwLEXRTbI20psHmxeMfhRuUPXQJMG5E+jwKepU0cKGjDd3ZqrD3W0SJCuvjyXVMfvGuga6Br4KjWgdZI30JVyfxrHekniJJAAiDfU/tIoJXToGjikBk4Oyazz6hroGugauCEauNA4udPD6U4O+GRpn5Nzz3tb10DXwEIamH3ik8hxmtR7tWuga6Br4MZrQAEPd7K4q/dUOb/isbsf3P9i3eQ/stvlfFU7dA10DRxKA013fDRBmagvlLigxIVnbkpz8ZZvjVyO4nLT6BKR2jp0DXQNdA10DXQNdA3sUQPaa9l32YsBfqjSA+i1Lib/Skf8KIlT1vCJ+f9I22mI1M9EwgAAAABJRU5ErkJggg==\n", "text/latex": [ "$\\displaystyle c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}$" ], @@ -763,7 +820,7 @@ }, { "data": { - "image/png": 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PK45ckgHuTkFyOTUhmUnfW8Z3iTsVfqTMm82O1HFN7Wu1gOZNe82xpQdsu9WSiW4R4732BTqpJJngI7NbW+rYyJoFmgWaBZoFmgV2bgHtUyQTPLQA/FtB1/IAdSn+OP7KRryp4GGP7+ser5lM8Joj/FsJKv2NBc7hp3v+RYKaDZoFmgWaBZoFmgWaBU7JAv8D3DpecfnLRiwAAAAASUVORK5CYII=", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}$" ], @@ -777,7 +834,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}$" ], @@ -790,7 +847,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/latex": [ "$\\displaystyle c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" ], @@ -804,12 +861,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{p}}^{\\mathrm{init}} = \\text{Positive electrode porosity}$" + "$\\displaystyle F = \\text{Faraday constant [C.mol-1]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{p}}__{\\mathrm{init}} = \\text{Positive electrode porosity}" + "F = \\text{Faraday constant [C.mol-1]}" ] }, "metadata": {}, @@ -817,12 +874,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{s}}^{\\mathrm{init}} = \\text{Separator porosity}$" + "$\\displaystyle L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{s}}__{\\mathrm{init}} = \\text{Separator porosity}" + "L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}" ] }, "metadata": {}, @@ -830,12 +887,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{n}}^{\\mathrm{init}} = \\text{Negative electrode porosity}$" + "$\\displaystyle L_{\\mathrm{s}} = \\text{Separator thickness [m]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{n}}__{\\mathrm{init}} = \\text{Negative electrode porosity}" + "L_{\\mathrm{s}} = \\text{Separator thickness [m]}" ] }, "metadata": {}, @@ -843,13 +900,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentr\n", - "ation [mol.m-3]}" + "L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}" ] }, "metadata": {}, @@ -857,13 +913,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{z} = \\text{Electrode height [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mo\n", - "l.m-3]}" + "L_z = \\text{Electrode height [m]}" ] }, "metadata": {}, @@ -871,13 +926,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{y} = \\text{Electrode width [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentr\n", - "ation [mol.m-3]}" + "L_y = \\text{Electrode width [m]}" ] }, "metadata": {}, @@ -885,12 +939,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}$" + "$\\displaystyle n_{electrodes parallel} = \\text{Number of electrodes connected in parallel to make a cell}$" ], "text/plain": [ - "D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}" + "n_electrodes_parallel = \\text{Number of electrodes connected in parallel to ma\n", + "ke a cell}" ] }, "metadata": {}, @@ -898,13 +953,12 @@ }, { "data": { - "image/png": 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", + "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle t_{\\mathrm{+}} = \\text{Cation transference number}$" ], "text/plain": [ - "b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte\n", - ")}" + "t_{\\mathrm{+}} = \\text{Cation transference number}" ] }, "metadata": {}, @@ -912,12 +966,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle \\epsilon_{\\mathrm{p}}^{\\mathrm{init}} = \\text{Positive electrode porosity}$" ], "text/plain": [ - "b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}" + "\\epsilon_{\\mathrm{p}}__{\\mathrm{init}} = \\text{Positive electrode porosity}" ] }, "metadata": {}, @@ -925,13 +979,12 @@ }, { "data": { - "image/png": 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", + "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle \\epsilon_{\\mathrm{s}}^{\\mathrm{init}} = \\text{Separator porosity}$" ], "text/plain": [ - "b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte\n", - ")}" + "\\epsilon_{\\mathrm{s}}__{\\mathrm{init}} = \\text{Separator porosity}" ] }, "metadata": {}, @@ -939,12 +992,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle F = \\text{Faraday constant [C.mol-1]}$" + "$\\displaystyle \\epsilon_{\\mathrm{n}}^{\\mathrm{init}} = \\text{Negative electrode porosity}$" ], "text/plain": [ - "F = \\text{Faraday constant [C.mol-1]}" + "\\epsilon_{\\mathrm{n}}__{\\mathrm{init}} = \\text{Negative electrode porosity}" ] }, "metadata": {}, @@ -952,12 +1005,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle R_{\\mathrm{p}} = \\text{Positive particle radius [m]}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "R_{\\mathrm{p}} = \\text{Positive particle radius [m]}" + "(\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentr\n", + "ation [mol.m-3]}" ] }, "metadata": {}, @@ -965,13 +1019,13 @@ }, { "data": { - "image/png": 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OmxM/G7ITvRVtHYsXtwSDj4damKk9Gyfv6u1jkZbmvW5HoCPQEegI3GIEtHdw82y30nv9Fu9UYI77NgdWbrUfDxyFY+ykFOYVQz81HaNxuk4dgY5AR6AjcPII/B9YGgOaaoCs7gAAAABJRU5ErkJggg==\n", "text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume fraction}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume frac\n", - "tion}" + "(\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mo\n", + "l.m-3]}" ] }, "metadata": {}, @@ -979,12 +1033,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}" + "(\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentr\n", + "ation [mol.m-3]}" ] }, "metadata": {}, @@ -992,12 +1047,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle L_{z} = \\text{Electrode height [m]}$" + "$\\displaystyle D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}$" ], "text/plain": [ - "L_z = \\text{Electrode height [m]}" + "D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}" ] }, "metadata": {}, @@ -1005,12 +1060,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle L_{y} = \\text{Electrode width [m]}$" + "$\\displaystyle b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte)}$" ], "text/plain": [ - "L_y = \\text{Electrode width [m]}" + "b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte\n", + ")}" ] }, "metadata": {}, @@ -1018,13 +1074,26 @@ }, { "data": { - "image/png": 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", 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A9EnelY6++1tdkAPrINqFgxrX3/Zf0tWlVL+8JuJ1Jd8ybPa2kIohCwvvCi9h3KBZoFmgWaBZYH8WUGzlRMphxW4C98e8caq2gOzPjWzng7t9LqRc4YZjbrVEDbFZoFmgWaBZoFnghlvgXz2gtRaeko6kAAAAAElFTkSuQmCC\n", "text/latex": [ - "$\\displaystyle n_{electrodes parallel} = \\text{Number of electrodes connected in parallel to make a cell}$" + "$\\displaystyle b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}$" ], "text/plain": [ - "n_electrodes_parallel = \\text{Number of electrodes connected in parallel to ma\n", - "ke a cell}" + "b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/latex": [ + "$\\displaystyle b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte)}$" + ], + "text/plain": [ + "b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte\n", + ")}" ] }, "metadata": {}, @@ -1032,7 +1101,34 @@ }, { "data": { - "image/png": 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+ "image/png": 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+ "text/latex": [ + "$\\displaystyle R_{\\mathrm{p}} = \\text{Positive particle radius [m]}$" + ], + "text/plain": [ + "R_{\\mathrm{p}} = \\text{Positive particle radius [m]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume fraction}$" + ], + "text/plain": [ + "\\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume frac\n", + "tion}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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"text/latex": [ "$\\displaystyle R_{\\mathrm{n}} = \\text{Negative particle radius [m]}$" ], @@ -1045,7 +1141,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\epsilon_{\\mathrm{s,n}} = \\text{Negative electrode active material volume fraction}$" ], @@ -1059,12 +1155,14 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}$" + "$\\displaystyle i_{\\mathrm{e}} = \\begin{cases}\\frac{i_{\\mathrm{cell}} x_{n}}{L_{\\mathrm{n}}}\\\\i_{\\mathrm{cell}}\\\\\\frac{i_{\\mathrm{cell}} \\left(L_{x} - x_{p}\\right)}{L_{\\mathrm{p}}}\\end{cases}$" ], "text/plain": [ - "L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}" + "i_{\\mathrm{e}} = \\begin{cases}\\frac{i_{\\mathrm{cell}} x_{n}}{L_{\\mathrm{n}}}\\\\\n", + "i_{\\mathrm{cell}}\\\\\\frac{i_{\\mathrm{cell}} \\left(L_{x} - x_{p}\\right)}{L_{\\mat\n", + "hrm{p}}}\\end{cases}" ] }, "metadata": {}, @@ -1072,12 +1170,12 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ - "$\\displaystyle t_{\\mathrm{+}} = \\text{Cation transference number}$" + "$\\displaystyle L_{x} = L_{\\mathrm{n}} + L_{\\mathrm{p}} + L_{\\mathrm{s}}$" ], "text/plain": [ - "t_{\\mathrm{+}} = \\text{Cation transference number}" + "Lₓ = L_{\\mathrm{n}} + L_{\\mathrm{p}} + L_{\\mathrm{s}}" ] }, "metadata": {}, @@ -1085,14 +1183,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\end{cases}}$" + "$\\displaystyle i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}$" ], "text/plain": [ - "c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\math\n", - "rm{n}}_{\\mathrm{s}}_{\\mathrm{p}}__{\\mathrm{init}}\\\\\\epsilon__{\\mathrm{init}}\\\\\n", - "\\epsilon__{\\mathrm{init}}\\end{cases}}" + "i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}" ] }, "metadata": {}, @@ -1100,13 +1196,12 @@ }, { "data": { - "image/png": "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", + "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilon c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}$" + "$\\displaystyle A_{\\mathrm{cc}} = L_{y} L_{z}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilo\n", - "n c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}" + "A_{\\mathrm{cc}} = L_{y} L_{z}" ] }, "metadata": {}, @@ -1114,15 +1209,14 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon^{b_e} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\\\\\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\end{cases}$" + "$\\displaystyle c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\end{cases}}$" ], "text/plain": [ - "\\epsilon_e}__{b = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}_{\\mathrm{e,n}}}\\\\\\l\n", - "eft(\\epsilon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}_{\\mathr\n", - "m{e,p}}}\\end{cases}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{\n", - "\\mathrm{init}}\\right)__{b" + "c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\math\n", + "rm{n}}_{\\mathrm{s}}_{\\mathrm{p}}__{\\mathrm{init}}\\\\\\epsilon__{\\mathrm{init}}\\\\\n", + "\\epsilon__{\\mathrm{init}}\\end{cases}}" ] }, "metadata": {}, @@ -1130,13 +1224,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilon c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}$" ], "text/plain": [ - "j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\math\n", - "rm{p}}}" + "(\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilo\n", + "n c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}" ] }, "metadata": {}, @@ -1144,12 +1238,15 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn$" + "$\\displaystyle \\mathcal{B} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\\\\\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\end{cases}$" ], "text/plain": [ - "\\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn" + "\\mathcal{B} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}_{\\mathrm{e,n}}}\\\\\\left(\n", + "\\epsilon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}_{\\mathrm{e,\n", + "p}}}\\end{cases}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{\\mat\n", + "hrm{init}}\\right)__{b" ] }, "metadata": {}, @@ -1157,12 +1254,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}$" + "$\\displaystyle aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases}$" ], "text/plain": [ - "a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}" + "aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathr\n", + "m{p}}\\end{cases}" ] }, "metadata": {}, @@ -1170,12 +1268,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}$" + "$\\displaystyle j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}$" ], "text/plain": [ - "i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}" + "j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\math\n", + "rm{p}}}" ] }, "metadata": {}, @@ -1183,12 +1282,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle A_{\\mathrm{cc}} = L_{y} L_{z}$" + "$\\displaystyle \\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn$" ], "text/plain": [ - "A_{\\mathrm{cc}} = L_{y} L_{z}" + "\\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn" ] }, "metadata": {}, @@ -1196,13 +1295,12 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ - "$\\displaystyle j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}$" + "$\\displaystyle a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}$" ], "text/plain": [ - "j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm\n", - "{n}}}" + "a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}" ] }, "metadata": {}, @@ -1210,12 +1308,13 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/latex": [ - "$\\displaystyle \\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn$" + "$\\displaystyle j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}$" ], "text/plain": [ - "\\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn" + "j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm\n", + "{n}}}" ] }, "metadata": {}, @@ -1223,12 +1322,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}$" + "$\\displaystyle \\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn$" ], "text/plain": [ - "a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}" + "\\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn" ] }, "metadata": {}, @@ -1236,13 +1335,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases}$" + "$\\displaystyle a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}$" ], "text/plain": [ - "aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathr\n", - "m{p}}\\end{cases}" + "a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}" ] }, "metadata": {}, @@ -1256,7 +1354,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1274,9 +1371,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1] Charles R. Harris, K. Jarrod Millman, Stéfan 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", - "[2] 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", - "[3] 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", + "[1] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[2] Charles R. Harris, K. Jarrod Millman, Stéfan 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", + "[3] 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", + "[4] 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", "\n" ] } @@ -1288,7 +1386,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1302,7 +1400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index e9e42fc447..536a355395 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -11,16 +11,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Pybamm models utilize a ratio that we refer to as \"transport efficiency\" which can be applied to transport co-efficients such as the diffusivity in the electrolyte that relates the effective transport property through a porous media comprised of a conducting and non-conducting phase to that of the transport through the bulk of the conducting phase\n", + "PyBaMM models utilize a ratio that we refer to as \"transport efficiency\" $\\mathcal{B}$ which can be applied to transport co-efficients such as the diffusivity in the electrolyte that relates the effective transport property through a porous media comprised of a conducting and non-conducting phase to that of the transport through the bulk of the conducting phase:\n", "$$\n", - "B = \\frac{X_{eff}}{X_0} = \\frac{\\epsilon}{\\tau},\n", + "\\mathcal{B} = \\frac{X_{eff}}{X_0} = \\frac{\\epsilon}{\\tau},\n", "$$\n", "\n", "Where $\\epsilon$ is the volume fraction of the conducting phase, the porosity of the electrode for diffusion within the electrolyte, and $\\tau$ is the tortuosity factor. A measure of the effect of the increased pathlength that transported species traverse due to the presence of obstacles.\n", "\n", "The tortuosity and tortuosity factor are often used interchangably but this can lead to confusion. Tortusosity is a purely geometric concept relating the length of a winding capillary pathway through a medium with the length of that medium, whereas tortuosity factor relates the the ratio of the transport property which may also depend on other factors such as anisotropic obstacles, boundary conditions of flow and also other physical phenomena such as the average pore size which could induce Knudsen effects. \n", "\n", - "In essence it is a \"fudge-factor\" but many studies have been devoted to its understanding and there are many relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10]. By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." + "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10] (see table below, confusingly tortuosity factor here is represented by $\\tau^2$ for reasons explored in the paper and $\\phi$ is used to represent the volume fraction of the conducting phase which is more general than the porosity). By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." ] }, { @@ -139,6 +139,13 @@ " 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "N.B the differences in the exponent constants used to modify the porosity and solid volume fraction. The existing Bruggeman model applies the exponent directly to the porosity $\\mathcal{B}=\\epsilon^{b}=\\epsilon^{3/2}$, the tortuosity factor model applies the tortuosity factor with includes the relation on porosity in this case $\\mathcal{B}=\\epsilon / \\tau = \\epsilon / \\epsilon^{-1/2} = \\epsilon^{3/2}$." + ] + }, { "cell_type": "code", "execution_count": 6, @@ -159,7 +166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7c1d18fcad1404189df6f04015d2a40", + "model_id": "240db810f1e0430696821ee44192b2b1", "version_major": 2, "version_minor": 0 }, @@ -173,7 +180,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -251,7 +258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "659e1c020ffb402886bc489053de7811", + "model_id": "cbdb93c1b74f434880b57dc8f8a0f3f6", "version_major": 2, "version_minor": 0 }, @@ -265,7 +272,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, diff --git a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py index 6c99cea328..c0582c013e 100644 --- a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py @@ -41,6 +41,6 @@ def _get_standard_transport_efficiency_variables(self, tor_dict): ) # Override print_name - tor.print_name = r"\epsilon^{b_e}" + tor.print_name = r"\mathcal{B}" return variables From ee27daf6beedd8d3244348a726f27b3b60449b98 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sun, 15 Oct 2023 14:44:52 +0100 Subject: [PATCH 16/35] Split the models into separate classes --- .../notebooks/models/tortuosity_models.ipynb | 81 +++++++++++++++ .../transport_efficiency/bruggeman.rst | 5 + .../cation_exchange_membrane.rst | 5 + .../general_transport_efficiency.rst | 5 - .../heterogeneous_catalyst.rst | 5 + .../hyperbola_of_revolution.rst | 5 + .../submodels/transport_efficiency/index.rst | 9 +- .../transport_efficiency/ordered_packing.rst | 5 + .../overlapping_spheres.rst | 5 + .../random_overlapping_cylinders.rst | 5 + .../tortuosity_factor.rst | 5 + .../notebooks/models/tortuosity_models.ipynb | 10 +- .../full_battery_models/base_battery_model.py | 98 +++++++++++++++++-- .../transport_efficiency/__init__.py | 9 +- .../transport_efficiency/bruggeman.py | 50 ++++++++++ .../cation_exchange_membrane.py | 46 +++++++++ .../general_transport_efficiency.py | 83 ---------------- .../heterogeneous_catalyst.py | 46 +++++++++ .../hyperbola_of_revolution.py | 46 +++++++++ .../transport_efficiency/ordered_packing.py | 46 +++++++++ .../overlapping_spheres.py | 46 +++++++++ .../random_overlapping_cylinders.py | 46 +++++++++ .../transport_efficiency/tortuosity_factor.py | 46 +++++++++ 23 files changed, 602 insertions(+), 105 deletions(-) create mode 100644 .virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb create mode 100644 docs/source/api/models/submodels/transport_efficiency/bruggeman.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst delete mode 100644 docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst create mode 100644 docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst create mode 100644 pybamm/models/submodels/transport_efficiency/bruggeman.py create mode 100644 pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py delete mode 100644 pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py create mode 100644 pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py create mode 100644 pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py create mode 100644 pybamm/models/submodels/transport_efficiency/ordered_packing.py create mode 100644 pybamm/models/submodels/transport_efficiency/overlapping_spheres.py create mode 100644 pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py create mode 100644 pybamm/models/submodels/transport_efficiency/tortuosity_factor.py diff --git a/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb new file mode 100644 index 0000000000..f55d6f2c89 --- /dev/null +++ b/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -0,0 +1,81 @@ + + + + + + + + + + + + + + + +import pybamm +import numpy as np + + +sols = [] +te_opts = pybamm.BatteryModelOptions({}).possible_options["transport efficiency"] +parameter_values = pybamm.ParameterValues("Marquis2019") +print(te_opts) + + +parameter_values.search("porosity") + + +parameter_values.search("Bruggeman") + + + + + +parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5), + 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5), + 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5), + 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5), + 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False) + + + + + +for t_label in te_opts: + model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model + sim = pybamm.Simulation(model, parameter_values=parameter_values) + sols.append(sim.solve([0, 3600])) # solve for 1 hour + + +pybamm.dynamic_plot(sols,labels=te_opts) + + + + + +np.allclose(sols[0]["Terminal voltage [V]"].data, sols[4]["Terminal voltage [V]"].data) + + + + + +parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0, + 'Positive electrode tortuosity factor (electrolyte)': 4.0, + 'Negative electrode tortuosity factor (electrode)': 3.0, + 'Positive electrode tortuosity factor (electrode)': 3.0, + 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False) + + +model = pybamm.lithium_ion.DFN(options={'transport efficiency': "tortuosity factor"}) # Doyle-Fuller-Newman model +sim = pybamm.Simulation(model, parameter_values=parameter_values) +sols.append(sim.solve([0, 3600])) + + +pybamm.dynamic_plot(sols,labels=te_opts+["higher tortuosity factor"]) + + + + + +pybamm.print_citations() diff --git a/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst new file mode 100644 index 0000000000..de3698e32a --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst @@ -0,0 +1,5 @@ +Bruggeman Transport Efficiency Model +==================================== + +.. autoclass:: pybamm.transport_efficiency.Bruggeman + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst new file mode 100644 index 0000000000..aaed6aa328 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst @@ -0,0 +1,5 @@ +Cation-Exchange Membrane Transport Efficiency Model +=================================================== + +.. autoclass:: pybamm.transport_efficiency.CationExchangeMembrane + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst b/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst deleted file mode 100644 index ef22d8868b..0000000000 --- a/docs/source/api/models/submodels/transport_efficiency/general_transport_efficiency.rst +++ /dev/null @@ -1,5 +0,0 @@ -General Transport Efficiency Model -================================== - -.. autoclass:: pybamm.transport_efficiency.GeneralTransportEfficiency - :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst new file mode 100644 index 0000000000..bca491cd8f --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst @@ -0,0 +1,5 @@ +Heterogeneous Catalyst Transport Efficiency Model +================================================= + +.. autoclass:: pybamm.transport_efficiency.HeterogeneousCatalyst + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst new file mode 100644 index 0000000000..a9736e0697 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst @@ -0,0 +1,5 @@ +Hyperbola of Revolution Transport Efficiency Model +================================================== + +.. autoclass:: pybamm.transport_efficiency.HyperbolaOfRevolution + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/index.rst b/docs/source/api/models/submodels/transport_efficiency/index.rst index 8bd8c56905..51f1d43065 100644 --- a/docs/source/api/models/submodels/transport_efficiency/index.rst +++ b/docs/source/api/models/submodels/transport_efficiency/index.rst @@ -5,4 +5,11 @@ transport_efficiency :maxdepth: 1 base_transport_efficiency - general_transport_efficiency + bruggeman + cation_exchange_membrane + heterogeneous_catalyst + hyperbola_of_revolution + ordered_packing + overlapping_spheres + random_overlapping_cylinders + tortuosity_factor diff --git a/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst new file mode 100644 index 0000000000..7d274c0b85 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst @@ -0,0 +1,5 @@ +Ordered Packing Transport Efficiency Model +========================================== + +.. autoclass:: pybamm.transport_efficiency.OrderedPacking + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst new file mode 100644 index 0000000000..be66ced2e8 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst @@ -0,0 +1,5 @@ +Overlapping Spheres Transport Efficiency Model +============================================== + +.. autoclass:: pybamm.transport_efficiency.OverlappingSpheres + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst new file mode 100644 index 0000000000..02e583544c --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst @@ -0,0 +1,5 @@ +Random Overlapping Cylinders Transport Efficiency Model +======================================================= + +.. autoclass:: pybamm.transport_efficiency.RandomOverlappingCylinders + :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst new file mode 100644 index 0000000000..75afa972ce --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst @@ -0,0 +1,5 @@ +Tortuosity Factor Transport Efficiency Model +============================================ + +.. autoclass:: pybamm.transport_efficiency.TortuosityFactor + :members: diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index 536a355395..6fb3a22ae5 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -166,7 +166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "240db810f1e0430696821ee44192b2b1", + "model_id": "93cb2f124bd348a0bf99249423a14717", "version_major": 2, "version_minor": 0 }, @@ -180,7 +180,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -258,7 +258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbdb93c1b74f434880b57dc8f8a0f3f6", + "model_id": "abd08396d3434395833472560a7114d1", "version_major": 2, "version_minor": 0 }, @@ -272,7 +272,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -338,7 +338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 6afaa642f8..e8312634e6 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -1153,16 +1153,94 @@ def set_external_circuit_submodel(self): self.submodels["external circuit"] = model def set_transport_efficiency_submodels(self): - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.GeneralTransportEfficiency( - self.param, "Electrolyte", self.options - ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.GeneralTransportEfficiency( - self.param, "Electrode", self.options - ) + if self.options["transport efficiency"] == "Bruggeman": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.Bruggeman( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.Bruggeman( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "tortuosity factor": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "ordered packing": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "hyperbola of revolution": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "overlapping spheres": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "random overlapping cylinders": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "heterogeneous catalyst": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrode", self.options + ) + elif self.options["transport efficiency"] == "cation-exchange membrane": + self.submodels[ + "electrolyte transport efficiency" + ] = pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrolyte", self.options + ) + self.submodels[ + "electrode transport efficiency" + ] = pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrode", self.options + ) def set_thermal_submodel(self): if self.options["thermal"] == "isothermal": diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index f7864e5e64..60ab4b2f45 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,2 +1,9 @@ from .base_transport_efficiency import BaseModel -from .general_transport_efficiency import GeneralTransportEfficiency +from .bruggeman import Bruggeman +from .cation_exchange_membrane import CationExchangeMembrane +from .heterogeneous_catalyst import HeterogeneousCatalyst +from .hyperbola_of_revolution import HyperbolaOfRevolution +from .ordered_packing import OrderedPacking +from .overlapping_spheres import OverlappingSpheres +from .random_overlapping_cylinders import RandomOverlappingCylinders +from .tortuosity_factor import TortuosityFactor diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py new file mode 100644 index 0000000000..8ae4d90f22 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -0,0 +1,50 @@ +# +# Class for Bruggeman transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class Bruggeman(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + pybamm.citations.register("bruggeman1935berechnung") + b_k = self.param.domain_params[domain.split()[0]].b_e + tor_k = eps_k**b_k + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + pybamm.citations.register("bruggeman1935berechnung") + b_k = self.param.domain_params[domain.split()[0]].b_s + tor_k = phi_k**b_k + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables + + diff --git a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py new file mode 100644 index 0000000000..2a5e5dab3f --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py @@ -0,0 +1,46 @@ +# +# Class for cation-exchange membrane transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class CationExchangeMembrane(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("mackie1955diffusion") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = ((2 - eps_k) / eps_k)**2 + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = ((2 - phi_k) / phi_k)**2 + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py deleted file mode 100644 index ef0f0e3e18..0000000000 --- a/pybamm/models/submodels/transport_efficiency/general_transport_efficiency.py +++ /dev/null @@ -1,83 +0,0 @@ -# -# Class for Bruggemantransport_efficiency -# -import pybamm -from .base_transport_efficiency import BaseModel - - -class GeneralTransportEfficiency(BaseModel): - """Submodel for transport_efficiency - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - component : str - The material for the model ('electrolyte' or 'electrode'). - options : dict, optional - A dictionary of options to be passed to the model. - """ - - def __init__(self, param, component, options=None): - super().__init__(param, component, options=options) - - def _tortuosity_factor_model(self, eps_k): - pybamm.citations.register("shen2007critical") - if self.options["transport efficiency"] == "ordered packing": - pybamm.citations.register("akanni1987effective") - tor_k = (3 - eps_k)*0.5 - elif self.options["transport efficiency"] == "hyperbola of revolution": - pybamm.citations.register("petersen1958diffusion") - tor_k = 2 - eps_k - elif self.options["transport efficiency"] == "overlapping spheres": - pybamm.citations.register("weissberg1963effective") - tor_k = 1 - pybamm.Log(eps_k*0.5) - elif self.options["transport efficiency"] == "random overlapping cylinders": - pybamm.citations.register("tomadakis1993transport") - tor_k = 1 - pybamm.Log(eps_k) - elif self.options["transport efficiency"] == "heterogeneous catalyst": - pybamm.citations.register("beeckman1990mathematical") - tor_k = eps_k / (1 - (1- eps_k)**(1/3)) - elif self.options["transport efficiency"] == "cation-exchange membrane": - pybamm.citations.register("mackie1955diffusion") - tor_k = ((2 - eps_k) / eps_k)**2 - - return tor_k - - def get_coupled_variables(self, variables): - if self.component == "Electrolyte": - tor_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize() - eps_k = variables[f"{Domain} porosity"] - if self.options["transport efficiency"] == "Bruggeman": - pybamm.citations.register("bruggeman1935berechnung") - b_k = self.param.domain_params[domain.split()[0]].b_e - tor_k = eps_k**b_k - elif self.options["transport efficiency"] == "tortuosity factor": - tau_k = self.param.domain_params[domain.split()[0]].tau_e - tor_k = eps_k / tau_k - else: - tor_k = self._tortuosity_factor_model(eps_k) - tor_dict[domain] = tor_k - elif self.component == "Electrode": - tor_dict = {} - for domain in self.options.whole_cell_domains: - if domain == "separator": - tor_k = pybamm.FullBroadcast(0, "separator", "current collector") - else: - Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) - if self.options["transport efficiency"] == "Bruggeman": - pybamm.citations.register("bruggeman1935berechnung") - b_k = self.param.domain_params[domain.split()[0]].b_s - tor_k = phi_k**b_k - elif self.options["transport efficiency"] == "tortuosity factor": - tau_k = self.param.domain_params[domain.split()[0]].tau_s - tor_k = phi_k / tau_k - else: - tor_k = self._tortuosity_factor_model(phi_k) - tor_dict[domain] = tor_k - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - - return variables diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py new file mode 100644 index 0000000000..5b65b14946 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -0,0 +1,46 @@ +# +# Class for heterogeneous catalyst transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class HeterogeneousCatalyst(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("beeckman1990mathematical") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = eps_k / (1 - (1- eps_k)**(1/3)) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = phi_k / (1 - (1- phi_k)**(1/3)) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables \ No newline at end of file diff --git a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py new file mode 100644 index 0000000000..5db42f3c83 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py @@ -0,0 +1,46 @@ +# +# Class for hyperbola of revolution transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class HyperbolaOfRevolution(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("petersen1958diffusion") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 2 - eps_k + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = 2 - phi_k + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py new file mode 100644 index 0000000000..437a55b47b --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -0,0 +1,46 @@ +# +# Class for Ordered packing transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class OrderedPacking(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("akanni1987effective") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = (3 - eps_k)*0.5 + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = (3 - phi_k)*0.5 + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py new file mode 100644 index 0000000000..cafaf183c2 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -0,0 +1,46 @@ +# +# Class for overlapping spheres transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class OverlappingSpheres(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("weissberg1963effective") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(eps_k*0.5) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = 1 - pybamm.Log(phi_k*0.5) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py new file mode 100644 index 0000000000..d4c051594b --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -0,0 +1,46 @@ +# +# Class for random overlapping cylinders transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class RandomOverlappingCylinders(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("tomadakis1993transport") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(eps_k) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tor_k = 1 - pybamm.Log(phi_k) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py new file mode 100644 index 0000000000..fc0a8053d7 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -0,0 +1,46 @@ +# +# Class for tortuosity factor transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class TortuosityFactor(BaseModel): + """Submodel for transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tau_k = self.param.domain_params[domain.split()[0]].tau_e + tor_k = eps_k / tau_k + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = (1 - variables[f"{Domain} porosity"]) + tau_k = self.param.domain_params[domain.split()[0]].tau_s + tor_k = phi_k / tau_k + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables From e96ff9ab9d016aa0e44947d737910a372e889241 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 15 Oct 2023 13:45:05 +0000 Subject: [PATCH 17/35] style: pre-commit fixes --- pybamm/models/submodels/transport_efficiency/bruggeman.py | 2 -- .../submodels/transport_efficiency/heterogeneous_catalyst.py | 2 +- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py index 8ae4d90f22..0a5505b631 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -46,5 +46,3 @@ def get_coupled_variables(self, variables): variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) return variables - - diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py index 5b65b14946..4a19ed3d2a 100644 --- a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -43,4 +43,4 @@ def get_coupled_variables(self, variables): tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) - return variables \ No newline at end of file + return variables From 28e96dde5d366e4dc7a5f4ff87746fa573a07f24 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Mon, 13 Nov 2023 16:40:28 +0000 Subject: [PATCH 18/35] Update docs and add citations --- .../examples/notebooks/models/tortuosity_models.ipynb | 2 +- pybamm/models/submodels/transport_efficiency/bruggeman.py | 7 +++++-- .../transport_efficiency/cation_exchange_membrane.py | 5 ++++- .../transport_efficiency/heterogeneous_catalyst.py | 4 +++- .../transport_efficiency/hyperbola_of_revolution.py | 4 +++- .../submodels/transport_efficiency/ordered_packing.py | 6 ++++-- .../submodels/transport_efficiency/overlapping_spheres.py | 6 ++++-- .../transport_efficiency/random_overlapping_cylinders.py | 5 ++++- .../submodels/transport_efficiency/tortuosity_factor.py | 2 +- 9 files changed, 29 insertions(+), 12 deletions(-) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index 6fb3a22ae5..e6ba6d155d 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -338,7 +338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py index 0a5505b631..5c87afd3eb 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -6,8 +6,11 @@ class Bruggeman(BaseModel): - """Submodel for transport_efficiency - + """Submodel for Bruggeman transport_efficiency + Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen + substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus + isotropen substanzen. Annalen der physik, 416(7):636–664, 1935. + Parameters ---------- param : parameter class diff --git a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py index 2a5e5dab3f..25a0b272c8 100644 --- a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py +++ b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py @@ -6,7 +6,10 @@ class CationExchangeMembrane(BaseModel): - """Submodel for transport_efficiency + """Submodel for Cation Exchange Membrane transport_efficiency + JS Mackie and P Meares. The diffusion of electrolytes in a cation-exchange resin + membrane i. theoretical. Proceedings of the Royal Society of London. + Series A. Mathematical and Physical Sciences, 232(1191):498–509, 1955. Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py index 4a19ed3d2a..2238489878 100644 --- a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -6,7 +6,9 @@ class HeterogeneousCatalyst(BaseModel): - """Submodel for transport_efficiency + """Submodel for Heterogeneous Catalyst transport_efficiency + JW Beeckman. Mathematical description of heterogeneous materials. + Chemical engineering science, 45(8):2603–2610, 1990. Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py index 5db42f3c83..0968c9fe98 100644 --- a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py +++ b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py @@ -6,7 +6,9 @@ class HyperbolaOfRevolution(BaseModel): - """Submodel for transport_efficiency + """Submodel for Hyperbola of revolution transport_efficiency + EE Petersen. Diffusion in a pore of varying cross section. AIChE Journal, + 4(3):343–345, 1958. Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py index 437a55b47b..83d0bfbb6f 100644 --- a/pybamm/models/submodels/transport_efficiency/ordered_packing.py +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -6,8 +6,10 @@ class OrderedPacking(BaseModel): - """Submodel for transport_efficiency - + """Submodel for Ordered Packing transport_efficiency + KA Akanni, JW Evans, and IS Abramson. Effective transport coefficients in + heterogeneous media. Chemical Engineering Science, 42(8):1945–1954, 1987. + Parameters ---------- param : parameter class diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py index cafaf183c2..5319b49a76 100644 --- a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -6,8 +6,10 @@ class OverlappingSpheres(BaseModel): - """Submodel for transport_efficiency - + """Submodel for Overlapping Spheres transport_efficiency + Harold L Weissberg. Effective diffusion coefficient in porous media. + Journal of Applied Physics, 34(9):2636–2639, 1963. + Parameters ---------- param : parameter class diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py index d4c051594b..41ffc21e46 100644 --- a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -6,7 +6,10 @@ class RandomOverlappingCylinders(BaseModel): - """Submodel for transport_efficiency + """Submodel for Random Overlapping Cylinders transport_efficiency + Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random + arrays of freely overlapping cylinders with various orientation distributions. + The Journal of chemical physics, 98(1):616–626, 1993. Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py index fc0a8053d7..79a1390301 100644 --- a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -6,7 +6,7 @@ class TortuosityFactor(BaseModel): - """Submodel for transport_efficiency + """Submodel for user supplied tortusoity factor transport_efficiency Parameters ---------- From a68445a398da34999d0f64ab940146860db6a9e5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Nov 2023 16:41:24 +0000 Subject: [PATCH 19/35] style: pre-commit fixes --- pybamm/models/submodels/transport_efficiency/bruggeman.py | 2 +- pybamm/models/submodels/transport_efficiency/ordered_packing.py | 2 +- .../submodels/transport_efficiency/overlapping_spheres.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py index 5c87afd3eb..35f945ded9 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -10,7 +10,7 @@ class Bruggeman(BaseModel): Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935. - + Parameters ---------- param : parameter class diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py index 83d0bfbb6f..f7377260ce 100644 --- a/pybamm/models/submodels/transport_efficiency/ordered_packing.py +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -9,7 +9,7 @@ class OrderedPacking(BaseModel): """Submodel for Ordered Packing transport_efficiency KA Akanni, JW Evans, and IS Abramson. Effective transport coefficients in heterogeneous media. Chemical Engineering Science, 42(8):1945–1954, 1987. - + Parameters ---------- param : parameter class diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py index 5319b49a76..98d63afcbb 100644 --- a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -9,7 +9,7 @@ class OverlappingSpheres(BaseModel): """Submodel for Overlapping Spheres transport_efficiency Harold L Weissberg. Effective diffusion coefficient in porous media. Journal of Applied Physics, 34(9):2636–2639, 1963. - + Parameters ---------- param : parameter class From 66b05f5b6985cb9f2ad58318709ef86c645c9208 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 15 Nov 2023 10:44:10 +0000 Subject: [PATCH 20/35] Change docs to footcite --- pybamm/models/submodels/transport_efficiency/bruggeman.py | 6 ++---- .../transport_efficiency/cation_exchange_membrane.py | 6 ++---- .../transport_efficiency/heterogeneous_catalyst.py | 3 +-- .../transport_efficiency/hyperbola_of_revolution.py | 3 +-- .../submodels/transport_efficiency/ordered_packing.py | 3 +-- .../submodels/transport_efficiency/overlapping_spheres.py | 3 +-- .../transport_efficiency/random_overlapping_cylinders.py | 6 ++---- 7 files changed, 10 insertions(+), 20 deletions(-) diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py index 35f945ded9..d53b84f329 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -6,10 +6,8 @@ class Bruggeman(BaseModel): - """Submodel for Bruggeman transport_efficiency - Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen - substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus - isotropen substanzen. Annalen der physik, 416(7):636–664, 1935. + """Submodel for Bruggeman transport_efficiency, + :footcite:t:`bruggeman1935berechnung` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py index 25a0b272c8..ca992fd1b3 100644 --- a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py +++ b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py @@ -6,10 +6,8 @@ class CationExchangeMembrane(BaseModel): - """Submodel for Cation Exchange Membrane transport_efficiency - JS Mackie and P Meares. The diffusion of electrolytes in a cation-exchange resin - membrane i. theoretical. Proceedings of the Royal Society of London. - Series A. Mathematical and Physical Sciences, 232(1191):498–509, 1955. + """Submodel for Cation Exchange Membrane transport_efficiency, + :footcite:t:`bruggeman1935berechnung`, :footcite:t:`shen2007critical` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py index 2238489878..d69f122ae6 100644 --- a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -7,8 +7,7 @@ class HeterogeneousCatalyst(BaseModel): """Submodel for Heterogeneous Catalyst transport_efficiency - JW Beeckman. Mathematical description of heterogeneous materials. - Chemical engineering science, 45(8):2603–2610, 1990. + :footcite:t:`beeckman1990mathematical`, :footcite:t:`shen2007critical` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py index 0968c9fe98..45cdaaa4c4 100644 --- a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py +++ b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py @@ -7,8 +7,7 @@ class HyperbolaOfRevolution(BaseModel): """Submodel for Hyperbola of revolution transport_efficiency - EE Petersen. Diffusion in a pore of varying cross section. AIChE Journal, - 4(3):343–345, 1958. + :footcite:t:`petersen1958diffusion`, :footcite:t:`shen2007critical` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py index f7377260ce..b6475590a3 100644 --- a/pybamm/models/submodels/transport_efficiency/ordered_packing.py +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -7,8 +7,7 @@ class OrderedPacking(BaseModel): """Submodel for Ordered Packing transport_efficiency - KA Akanni, JW Evans, and IS Abramson. Effective transport coefficients in - heterogeneous media. Chemical Engineering Science, 42(8):1945–1954, 1987. + :footcite:t:`akanni1987effective`, :footcite:t:`shen2007critical` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py index 98d63afcbb..81bd2a7af9 100644 --- a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -7,8 +7,7 @@ class OverlappingSpheres(BaseModel): """Submodel for Overlapping Spheres transport_efficiency - Harold L Weissberg. Effective diffusion coefficient in porous media. - Journal of Applied Physics, 34(9):2636–2639, 1963. + :footcite:t:`weissberg1963effective`, :footcite:t:`shen2007critical` Parameters ---------- diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py index 41ffc21e46..c93fa71715 100644 --- a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -6,10 +6,8 @@ class RandomOverlappingCylinders(BaseModel): - """Submodel for Random Overlapping Cylinders transport_efficiency - Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random - arrays of freely overlapping cylinders with various orientation distributions. - The Journal of chemical physics, 98(1):616–626, 1993. + """Submodel for Random Overlapping Cylinders transport_efficiency, + :footcite:t:`tomadakis1993transport`, :footcite:t:`shen2007critical` Parameters ---------- From e15fbc7afe2a6ac190c5545273a03e6c288f146c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 15 Nov 2023 10:44:21 +0000 Subject: [PATCH 21/35] style: pre-commit fixes --- .../transport_efficiency/random_overlapping_cylinders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py index c93fa71715..daaa526173 100644 --- a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -6,7 +6,7 @@ class RandomOverlappingCylinders(BaseModel): - """Submodel for Random Overlapping Cylinders transport_efficiency, + """Submodel for Random Overlapping Cylinders transport_efficiency, :footcite:t:`tomadakis1993transport`, :footcite:t:`shen2007critical` Parameters From 9974367fdcc5b4ef533d853d54ee4a037483bc3c Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Wed, 15 Nov 2023 17:08:38 +0000 Subject: [PATCH 22/35] Update pybamm/models/submodels/transport_efficiency/tortuosity_factor.py Co-authored-by: Ferran Brosa Planella --- .../models/submodels/transport_efficiency/tortuosity_factor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py index 79a1390301..95b2571dc1 100644 --- a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -6,7 +6,7 @@ class TortuosityFactor(BaseModel): - """Submodel for user supplied tortusoity factor transport_efficiency + """Submodel for user supplied tortuosity factor transport_efficiency Parameters ---------- From 5b66b8887166d6ee3ddd287cda34d97bbb8e6c86 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 20 Jan 2024 10:29:55 +0000 Subject: [PATCH 23/35] style: pre-commit fixes --- .../notebooks/models/tortuosity_models.ipynb | 42 ++++++++++++------- .../transport_efficiency/bruggeman.py | 2 +- .../cation_exchange_membrane.py | 6 +-- .../heterogeneous_catalyst.py | 6 +-- .../hyperbola_of_revolution.py | 2 +- .../transport_efficiency/ordered_packing.py | 6 +-- .../overlapping_spheres.py | 6 +-- .../random_overlapping_cylinders.py | 2 +- .../transport_efficiency/tortuosity_factor.py | 2 +- 9 files changed, 44 insertions(+), 30 deletions(-) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index e6ba6d155d..cde6dc652f 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -132,11 +132,16 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", - " 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", - " 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", - " 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", - " 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False)" + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Negative electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Separator tortuosity factor (electrolyte)\": 1.0,\n", + " },\n", + " check_already_exists=False,\n", + ")" ] }, { @@ -153,7 +158,9 @@ "outputs": [], "source": [ "for t_label in te_opts:\n", - " model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model\n", + " model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": t_label}\n", + " ) # Doyle-Fuller-Newman model\n", " sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", " sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] @@ -189,7 +196,7 @@ } ], "source": [ - "pybamm.dynamic_plot(sols,labels=te_opts)" + "pybamm.dynamic_plot(sols, labels=te_opts)" ] }, { @@ -232,11 +239,16 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0,\n", - " 'Positive electrode tortuosity factor (electrolyte)': 4.0,\n", - " 'Negative electrode tortuosity factor (electrode)': 3.0,\n", - " 'Positive electrode tortuosity factor (electrode)': 3.0,\n", - " 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False)" + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Negative electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Positive electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Separator tortuosity factor (electrolyte)\": 1.5,\n", + " },\n", + " check_already_exists=False,\n", + ")" ] }, { @@ -245,7 +257,9 @@ "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN(options={'transport efficiency': \"tortuosity factor\"}) # Doyle-Fuller-Newman model\n", + "model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": \"tortuosity factor\"}\n", + ") # Doyle-Fuller-Newman model\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", "sols.append(sim.solve([0, 3600]))" ] @@ -281,7 +295,7 @@ } ], "source": [ - "pybamm.dynamic_plot(sols,labels=te_opts+[\"higher tortuosity factor\"])" + "pybamm.dynamic_plot(sols, labels=te_opts + [\"higher tortuosity factor\"])" ] }, { diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py index d53b84f329..ec26d7955d 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -39,7 +39,7 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) + phi_k = 1 - variables[f"{Domain} porosity"] pybamm.citations.register("bruggeman1935berechnung") b_k = self.param.domain_params[domain.split()[0]].b_s tor_k = phi_k**b_k diff --git a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py index ca992fd1b3..3ffb57e7de 100644 --- a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py +++ b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py @@ -30,7 +30,7 @@ def get_coupled_variables(self, variables): for domain in self.options.whole_cell_domains: Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] - tor_k = ((2 - eps_k) / eps_k)**2 + tor_k = ((2 - eps_k) / eps_k) ** 2 tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} @@ -39,8 +39,8 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) - tor_k = ((2 - phi_k) / phi_k)**2 + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = ((2 - phi_k) / phi_k) ** 2 tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py index d69f122ae6..7ec8bc3580 100644 --- a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -30,7 +30,7 @@ def get_coupled_variables(self, variables): for domain in self.options.whole_cell_domains: Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] - tor_k = eps_k / (1 - (1- eps_k)**(1/3)) + tor_k = eps_k / (1 - (1 - eps_k) ** (1 / 3)) tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} @@ -39,8 +39,8 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) - tor_k = phi_k / (1 - (1- phi_k)**(1/3)) + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = phi_k / (1 - (1 - phi_k) ** (1 / 3)) tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py index 45cdaaa4c4..306c66b774 100644 --- a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py +++ b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py @@ -39,7 +39,7 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) + phi_k = 1 - variables[f"{Domain} porosity"] tor_k = 2 - phi_k tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py index b6475590a3..13b3a3515e 100644 --- a/pybamm/models/submodels/transport_efficiency/ordered_packing.py +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -30,7 +30,7 @@ def get_coupled_variables(self, variables): for domain in self.options.whole_cell_domains: Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] - tor_k = (3 - eps_k)*0.5 + tor_k = (3 - eps_k) * 0.5 tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} @@ -39,8 +39,8 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) - tor_k = (3 - phi_k)*0.5 + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = (3 - phi_k) * 0.5 tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py index 81bd2a7af9..9bbed1fd05 100644 --- a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -30,7 +30,7 @@ def get_coupled_variables(self, variables): for domain in self.options.whole_cell_domains: Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] - tor_k = 1 - pybamm.Log(eps_k*0.5) + tor_k = 1 - pybamm.Log(eps_k * 0.5) tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} @@ -39,8 +39,8 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) - tor_k = 1 - pybamm.Log(phi_k*0.5) + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(phi_k * 0.5) tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py index daaa526173..da32f2f4fe 100644 --- a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -39,7 +39,7 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) + phi_k = 1 - variables[f"{Domain} porosity"] tor_k = 1 - pybamm.Log(phi_k) tor_dict[domain] = tor_k variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py index 95b2571dc1..0f5686e476 100644 --- a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -37,7 +37,7 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - phi_k = (1 - variables[f"{Domain} porosity"]) + phi_k = 1 - variables[f"{Domain} porosity"] tau_k = self.param.domain_params[domain.split()[0]].tau_s tor_k = phi_k / tau_k tor_dict[domain] = tor_k From cf9e796d42e7cafa74fdde6d94d79e1f4f96424c Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 20 Jan 2024 10:41:42 +0000 Subject: [PATCH 24/35] Remove image from notebook --- .../notebooks/models/tortuosity_models.ipynb | 3 --- .../notebooks/models/tortuosity_models.ipynb | 16 ++-------------- 2 files changed, 2 insertions(+), 17 deletions(-) diff --git a/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb index f55d6f2c89..a4862cc1e3 100644 --- a/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -10,9 +10,6 @@ - - - import pybamm import numpy as np diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index e6ba6d155d..97a250414b 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -20,19 +20,7 @@ "\n", "The tortuosity and tortuosity factor are often used interchangably but this can lead to confusion. Tortusosity is a purely geometric concept relating the length of a winding capillary pathway through a medium with the length of that medium, whereas tortuosity factor relates the the ratio of the transport property which may also depend on other factors such as anisotropic obstacles, boundary conditions of flow and also other physical phenomena such as the average pore size which could induce Knudsen effects. \n", "\n", - "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10] (see table below, confusingly tortuosity factor here is represented by $\\tau^2$ for reasons explored in the paper and $\\phi$ is used to represent the volume fraction of the conducting phase which is more general than the porosity). By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." - ] - }, - { - "attachments": { - "c46a76fa-4c2b-46ff-a51a-0498e38e118b.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![image.png](attachment:c46a76fa-4c2b-46ff-a51a-0498e38e118b.png)" + "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10]. By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." ] }, { @@ -338,7 +326,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.5" } }, "nbformat": 4, From fa889853f89035805479d3ed0beeb6af86941ce5 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 20 Jan 2024 10:45:31 +0000 Subject: [PATCH 25/35] remove .virtual_documents --- .../notebooks/models/tortuosity_models.ipynb | 78 ------------------- 1 file changed, 78 deletions(-) delete mode 100644 .virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb diff --git a/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb deleted file mode 100644 index a4862cc1e3..0000000000 --- a/.virtual_documents/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ /dev/null @@ -1,78 +0,0 @@ - - - - - - - - - - - - -import pybamm -import numpy as np - - -sols = [] -te_opts = pybamm.BatteryModelOptions({}).possible_options["transport efficiency"] -parameter_values = pybamm.ParameterValues("Marquis2019") -print(te_opts) - - -parameter_values.search("porosity") - - -parameter_values.search("Bruggeman") - - - - - -parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5), - 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5), - 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5), - 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5), - 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False) - - - - - -for t_label in te_opts: - model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model - sim = pybamm.Simulation(model, parameter_values=parameter_values) - sols.append(sim.solve([0, 3600])) # solve for 1 hour - - -pybamm.dynamic_plot(sols,labels=te_opts) - - - - - -np.allclose(sols[0]["Terminal voltage [V]"].data, sols[4]["Terminal voltage [V]"].data) - - - - - -parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0, - 'Positive electrode tortuosity factor (electrolyte)': 4.0, - 'Negative electrode tortuosity factor (electrode)': 3.0, - 'Positive electrode tortuosity factor (electrode)': 3.0, - 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False) - - -model = pybamm.lithium_ion.DFN(options={'transport efficiency': "tortuosity factor"}) # Doyle-Fuller-Newman model -sim = pybamm.Simulation(model, parameter_values=parameter_values) -sols.append(sim.solve([0, 3600])) - - -pybamm.dynamic_plot(sols,labels=te_opts+["higher tortuosity factor"]) - - - - - -pybamm.print_citations() From 46cc6c813cd2a0eb2517bd75bd63b7ff83f1f2a9 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 20 Jan 2024 10:48:57 +0000 Subject: [PATCH 26/35] Update syntax for style --- .../notebooks/models/tortuosity_models.ipynb | 80 ++++++++----------- 1 file changed, 33 insertions(+), 47 deletions(-) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index aca9e7c5c8..032020740a 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -116,20 +116,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "parameter_values.update(\n", - " {\n", - " \"Negative electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", - " \"Positive electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", - " \"Negative electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", - " \"Positive electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", - " \"Separator tortuosity factor (electrolyte)\": 1.0,\n", - " },\n", - " check_already_exists=False,\n", - ")" + "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", + " 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", + " 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", + " 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", + " 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False)" ] }, { @@ -141,27 +136,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "for t_label in te_opts:\n", - " model = pybamm.lithium_ion.DFN(\n", - " options={\"transport efficiency\": t_label}\n", - " ) # Doyle-Fuller-Newman model\n", + " model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model\n", " sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", " sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93cb2f124bd348a0bf99249423a14717", + "model_id": "2a0d1356fdaf495c8898429e27e30478", "version_major": 2, "version_minor": 0 }, @@ -175,16 +168,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pybamm.dynamic_plot(sols, labels=te_opts)" + "pybamm.dynamic_plot(sols,labels=te_opts)" ] }, { @@ -196,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -205,7 +198,7 @@ "True" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -223,44 +216,37 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "parameter_values.update(\n", - " {\n", - " \"Negative electrode tortuosity factor (electrolyte)\": 4.0,\n", - " \"Positive electrode tortuosity factor (electrolyte)\": 4.0,\n", - " \"Negative electrode tortuosity factor (electrode)\": 3.0,\n", - " \"Positive electrode tortuosity factor (electrode)\": 3.0,\n", - " \"Separator tortuosity factor (electrolyte)\": 1.5,\n", - " },\n", - " check_already_exists=False,\n", - ")" + "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0,\n", + " 'Positive electrode tortuosity factor (electrolyte)': 4.0,\n", + " 'Negative electrode tortuosity factor (electrode)': 3.0,\n", + " 'Positive electrode tortuosity factor (electrode)': 3.0,\n", + " 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN(\n", - " options={\"transport efficiency\": \"tortuosity factor\"}\n", - ") # Doyle-Fuller-Newman model\n", + "model = pybamm.lithium_ion.DFN(options={'transport efficiency': \"tortuosity factor\"}) # Doyle-Fuller-Newman model\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", "sols.append(sim.solve([0, 3600]))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abd08396d3434395833472560a7114d1", + "model_id": "5292e8b8c6884c199cf5023cf42ea3ac", "version_major": 2, "version_minor": 0 }, @@ -274,16 +260,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pybamm.dynamic_plot(sols, labels=te_opts + [\"higher tortuosity factor\"])" + "pybamm.dynamic_plot(sols,labels=[*te_opts, \"higher tortuosity factor\"])" ] }, { @@ -295,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { From cefec687fe408ecd00beec6d7e483ab2be25df08 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 20 Jan 2024 10:49:08 +0000 Subject: [PATCH 27/35] style: pre-commit fixes --- .../notebooks/models/tortuosity_models.ipynb | 42 ++++++++++++------- 1 file changed, 28 insertions(+), 14 deletions(-) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index 032020740a..7cc85d43fc 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -120,11 +120,16 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", - " 'Positive electrode tortuosity factor (electrolyte)': 0.3**(-0.5),\n", - " 'Negative electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", - " 'Positive electrode tortuosity factor (electrode)': 0.7**(-0.5),\n", - " 'Separator tortuosity factor (electrolyte)': 1.0}, check_already_exists=False)" + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Negative electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Separator tortuosity factor (electrolyte)\": 1.0,\n", + " },\n", + " check_already_exists=False,\n", + ")" ] }, { @@ -141,7 +146,9 @@ "outputs": [], "source": [ "for t_label in te_opts:\n", - " model = pybamm.lithium_ion.DFN(options={'transport efficiency': t_label}) # Doyle-Fuller-Newman model\n", + " model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": t_label}\n", + " ) # Doyle-Fuller-Newman model\n", " sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", " sols.append(sim.solve([0, 3600])) # solve for 1 hour" ] @@ -177,7 +184,7 @@ } ], "source": [ - "pybamm.dynamic_plot(sols,labels=te_opts)" + "pybamm.dynamic_plot(sols, labels=te_opts)" ] }, { @@ -220,11 +227,16 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values.update({'Negative electrode tortuosity factor (electrolyte)': 4.0,\n", - " 'Positive electrode tortuosity factor (electrolyte)': 4.0,\n", - " 'Negative electrode tortuosity factor (electrode)': 3.0,\n", - " 'Positive electrode tortuosity factor (electrode)': 3.0,\n", - " 'Separator tortuosity factor (electrolyte)': 1.5}, check_already_exists=False)" + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Negative electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Positive electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Separator tortuosity factor (electrolyte)\": 1.5,\n", + " },\n", + " check_already_exists=False,\n", + ")" ] }, { @@ -233,7 +245,9 @@ "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN(options={'transport efficiency': \"tortuosity factor\"}) # Doyle-Fuller-Newman model\n", + "model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": \"tortuosity factor\"}\n", + ") # Doyle-Fuller-Newman model\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", "sols.append(sim.solve([0, 3600]))" ] @@ -269,7 +283,7 @@ } ], "source": [ - "pybamm.dynamic_plot(sols,labels=[*te_opts, \"higher tortuosity factor\"])" + "pybamm.dynamic_plot(sols, labels=[*te_opts, \"higher tortuosity factor\"])" ] }, { From b586a5090685f747fc468e22c31a9f51dd95ed06 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 20 Jan 2024 11:13:58 +0000 Subject: [PATCH 28/35] Update index.rst --- docs/source/api/models/submodels/transport_efficiency/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/api/models/submodels/transport_efficiency/index.rst b/docs/source/api/models/submodels/transport_efficiency/index.rst index 51f1d43065..3149caad2c 100644 --- a/docs/source/api/models/submodels/transport_efficiency/index.rst +++ b/docs/source/api/models/submodels/transport_efficiency/index.rst @@ -1,4 +1,4 @@ -transport_efficiency +Transport Efficiency ==================== .. toctree:: From 3ff48fa9b32f1d42086f7f94796a3ec8c2cdf59b Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 20 Jan 2024 11:18:32 +0000 Subject: [PATCH 29/35] Adjust reference to Shen and Chen --- docs/source/examples/notebooks/models/tortuosity_models.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb index 7cc85d43fc..51023008cd 100644 --- a/docs/source/examples/notebooks/models/tortuosity_models.ipynb +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -20,7 +20,7 @@ "\n", "The tortuosity and tortuosity factor are often used interchangably but this can lead to confusion. Tortusosity is a purely geometric concept relating the length of a winding capillary pathway through a medium with the length of that medium, whereas tortuosity factor relates the the ratio of the transport property which may also depend on other factors such as anisotropic obstacles, boundary conditions of flow and also other physical phenomena such as the average pore size which could induce Knudsen effects. \n", "\n", - "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by Shen & Chen [10]. By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." + "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by [Shen & Chen](https://www.sciencedirect.com/science/article/abs/pii/S0009250907003144). By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." ] }, { From c3173c2a9576f261d0989d3d4d08b410fec6eb83 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 29 Apr 2024 09:22:17 +0000 Subject: [PATCH 30/35] style: pre-commit fixes --- .../full_battery_models/base_battery_model.py | 128 +++++++++--------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index fa4e3db3b7..37dd399991 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -1175,92 +1175,92 @@ def set_external_circuit_submodel(self): def set_transport_efficiency_submodels(self): if self.options["transport efficiency"] == "Bruggeman": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.Bruggeman( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.Bruggeman( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "tortuosity factor": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.TortuosityFactor( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.TortuosityFactor( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "ordered packing": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.OrderedPacking( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.OrderedPacking( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "hyperbola of revolution": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.HyperbolaOfRevolution( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.HyperbolaOfRevolution( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "overlapping spheres": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.OverlappingSpheres( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.OverlappingSpheres( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "random overlapping cylinders": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.RandomOverlappingCylinders( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.RandomOverlappingCylinders( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "heterogeneous catalyst": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.HeterogeneousCatalyst( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.HeterogeneousCatalyst( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrode", self.options + ) ) elif self.options["transport efficiency"] == "cation-exchange membrane": - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.CationExchangeMembrane( - self.param, "Electrolyte", self.options + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrolyte", self.options + ) ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.CationExchangeMembrane( - self.param, "Electrode", self.options + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrode", self.options + ) ) def set_thermal_submodel(self): From 506cf5c76da90670a7246332c80812d55f834ead Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Mon, 29 Apr 2024 13:56:45 +0100 Subject: [PATCH 31/35] Add footbibliography to rst files --- .../api/models/submodels/transport_efficiency/bruggeman.rst | 4 +++- .../transport_efficiency/cation_exchange_membrane.rst | 4 +++- .../transport_efficiency/heterogeneous_catalyst.rst | 5 ++++- .../transport_efficiency/hyperbola_of_revolution.rst | 4 +++- .../submodels/transport_efficiency/ordered_packing.rst | 4 +++- .../submodels/transport_efficiency/overlapping_spheres.rst | 4 +++- .../transport_efficiency/random_overlapping_cylinders.rst | 4 +++- .../submodels/transport_efficiency/tortuosity_factor.rst | 4 +++- 8 files changed, 25 insertions(+), 8 deletions(-) diff --git a/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst index de3698e32a..548427267b 100644 --- a/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst +++ b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst @@ -2,4 +2,6 @@ Bruggeman Transport Efficiency Model ==================================== .. autoclass:: pybamm.transport_efficiency.Bruggeman - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst index aaed6aa328..c769920cf4 100644 --- a/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst +++ b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst @@ -2,4 +2,6 @@ Cation-Exchange Membrane Transport Efficiency Model =================================================== .. autoclass:: pybamm.transport_efficiency.CationExchangeMembrane - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst index bca491cd8f..de5f8cd3f8 100644 --- a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst +++ b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst @@ -2,4 +2,7 @@ Heterogeneous Catalyst Transport Efficiency Model ================================================= .. autoclass:: pybamm.transport_efficiency.HeterogeneousCatalyst - :members: + :members: + +.. footbibliography:: + diff --git a/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst index a9736e0697..39f2078224 100644 --- a/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst +++ b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst @@ -2,4 +2,6 @@ Hyperbola of Revolution Transport Efficiency Model ================================================== .. autoclass:: pybamm.transport_efficiency.HyperbolaOfRevolution - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst index 7d274c0b85..d0164c983e 100644 --- a/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst +++ b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst @@ -2,4 +2,6 @@ Ordered Packing Transport Efficiency Model ========================================== .. autoclass:: pybamm.transport_efficiency.OrderedPacking - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst index be66ced2e8..546e15f63e 100644 --- a/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst +++ b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst @@ -2,4 +2,6 @@ Overlapping Spheres Transport Efficiency Model ============================================== .. autoclass:: pybamm.transport_efficiency.OverlappingSpheres - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst index 02e583544c..037468fabf 100644 --- a/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst +++ b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst @@ -2,4 +2,6 @@ Random Overlapping Cylinders Transport Efficiency Model ======================================================= .. autoclass:: pybamm.transport_efficiency.RandomOverlappingCylinders - :members: + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst index 75afa972ce..10913f3c5b 100644 --- a/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst +++ b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst @@ -2,4 +2,6 @@ Tortuosity Factor Transport Efficiency Model ============================================ .. autoclass:: pybamm.transport_efficiency.TortuosityFactor - :members: + :members: + +.. footbibliography:: From e4ab9c3e2a7181376306e1b34f1e4a9c2e743a41 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 29 Apr 2024 12:57:15 +0000 Subject: [PATCH 32/35] style: pre-commit fixes --- .../submodels/transport_efficiency/heterogeneous_catalyst.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst index de5f8cd3f8..7ee8c3326a 100644 --- a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst +++ b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst @@ -5,4 +5,3 @@ Heterogeneous Catalyst Transport Efficiency Model :members: .. footbibliography:: - From 8be88e93505130093ba29212f8efd278b4b28f76 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Fri, 3 May 2024 16:04:41 +0100 Subject: [PATCH 33/35] Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fa40559159..e3f07ec670 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,8 +2,8 @@ ## Features -- Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706])) +- Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) - Added functions for normal probability density function (`pybamm.normal_pdf`) and cumulative distribution function (`pybamm.normal_cdf`) ([#3999](https://github.com/pybamm-team/PyBaMM/pull/3999)) - Updates multiprocess `Pool` in `BaseSolver.solve()` to be constructed with context `fork`. Adds small example for multiprocess inputs. ([#3974](https://github.com/pybamm-team/PyBaMM/pull/3974)) From bb587c08a8d365a20c54a79caa1ed9d507fd17e1 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Sat, 4 May 2024 17:53:23 +0100 Subject: [PATCH 34/35] Update CHANGELOG.md Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> --- CHANGELOG.md | 1 - 1 file changed, 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 30753463be..bdce595628 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,6 @@ ## Features -- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706])) - Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) - Added functions for normal probability density function (`pybamm.normal_pdf`) and cumulative distribution function (`pybamm.normal_cdf`) ([#3999](https://github.com/pybamm-team/PyBaMM/pull/3999)) From 156a5f540b6176cff0dca2de82b234d8f63078e1 Mon Sep 17 00:00:00 2001 From: Tom Tranter Date: Mon, 13 May 2024 10:43:04 +0100 Subject: [PATCH 35/35] Update CITATIONS.bib --- pybamm/CITATIONS.bib | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index e9b1c65ac7..00d41fadd3 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -770,15 +770,16 @@ @article{shen2007critical pages={3748--3755}, year={2007}, publisher={Elsevier} +} @article{Wycisk2022, -title = {Modified Plett-model for modeling voltage hysteresis in lithium-ion cells}, -journal = {Journal of Energy Storage}, -volume = {52}, -pages = {105016}, -year = {2022}, -issn = {2352-152X}, -doi = {https://doi.org/10.1016/j.est.2022.105016}, -url = {https://www.sciencedirect.com/science/article/pii/S2352152X22010192}, -author = {Dominik Wycisk and Marc Oldenburger and Marc Gerry Stoye and Toni Mrkonjic and Arnulf Latz}, -keywords = {Lithium-ion battery, Voltage hysteresis, Plett-model, Silicon–graphite anode}, + title = {Modified Plett-model for modeling voltage hysteresis in lithium-ion cells}, + journal = {Journal of Energy Storage}, + volume = {52}, + pages = {105016}, + year = {2022}, + issn = {2352-152X}, + doi = {https://doi.org/10.1016/j.est.2022.105016}, + url = {https://www.sciencedirect.com/science/article/pii/S2352152X22010192}, + author = {Dominik Wycisk and Marc Oldenburger and Marc Gerry Stoye and Toni Mrkonjic and Arnulf Latz}, + keywords = {Lithium-ion battery, Voltage hysteresis, Plett-model, Silicon–graphite anode}, }