Launcher Trajectory Optimization Module (LaTOM)

This package has been developed by Alberto FOSSA' and Giuliana Elena MICELI as part of the MAE2 Research Project "Optimal Control of Trajectory of a Reusable Launcher in OpenMDAO/dymos"

Authors

• Alberto FOSSA'
• Giuliana Elena MICELI

Installation

• Refer to install.md for the installation instructions
• Refer to environment.md to properly setup the OpenMDAO/dymos environment

Documentation

• Package documentation
• Example scripts

Contents

This directory contains the source code to find the most fuel efficient transfer trajectory from the Moon surface to a specified Low Lunar Orbit (LLO) and back or from an LLO to an Highly Elliptical Orbit (HEO).

The optimization can be performed in the following cases:

1. two-dimensional ascent trajectories:

• constant thrust
• variable thrust
• variable thrust and constrained minimum safe altitude

2. two-dimensional descent trajectories:

• constant thrust
• constant thrust and constrained vertical landing
• variable thrust
• variable thrust and constrained minimum safe altitude

3. two-dimensional LLO to HEO transfers:

• single phase trajectory with variable thrust
• three-phases trajectory with two powered phases at constant thrust and intermediate coasting arc
• single phase escape burn with constant thrust

Run a simulation and display the results

There are two ways for running a simulation and display the results:

• perform a new optimization to obtain your own solution starting from custom values for the different trajectory parameters. Multiple examples are included in scripts/computation and can be consulted here

• load a structure stored in one of the four data subdirectories that contain the results already obtained for an optimal transfer trajectory and simply display those results. Multiple examples are included in scripts/visualization and can be consulted here

In either case do the following:

1. open one of the scripts in the scripts subdirectories
2. read the list that describes the different possibilities and choose the appropriate settings
3. optionally define your own parameters to perform a new optimization
4. run the script and wait for the results to be displayed

References

Gray, Justin S., et al. ‘OpenMDAO: An Open-Source Framework for Multidisciplinary Design, Analysis, and Optimization’. Structural and Multidisciplinary Optimization, vol. 59, no. 4, Apr. 2019, pp. 1075–104. doi:10.1007/s00158-019-02211-z.

Hendricks, Eric S., et al. ‘Simultaneous Propulsion System and Trajectory Optimization’. 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, American Institute of Aeronautics and Astronautics, 2017. doi:10.2514/6.2017-4435.

HSL, A Collection of Fortran Codes for Large Scale Scientific Computation. http://www.hsl.rl.ac.uk/.

Perez, Ruben E., et al. ‘PyOpt: A Python-Based Object-Oriented Framework for Nonlinear Constrained Optimization’. Structural and Multidisciplinary Optimization, vol. 45, no. 1, Jan. 2012, pp. 101–18. DOI.org (Crossref), doi:10.1007/s00158-011-0666-3.

Wächter, Andreas, and Lorenz T. Biegler. ‘On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming’. Mathematical Programming, vol. 106, no. 1, Mar. 2006, pp. 25–57. doi:10.1007/s10107-004-0559-y.

M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019