Automated Multiple Gravity-Assist Sequence Optimisation

An intelligent parallel-computing methodology

Master Thesis (2023)
Author(s)

S.B. Cowan (TU Delft - Aerospace Engineering)

Contributor(s)

Ron Noomen – Mentor (TU Delft - Astrodynamics & Space Missions)

E Mooij – Graduation committee member (TU Delft - Astrodynamics & Space Missions)

E Kampen – Graduation committee member (TU Delft - Control & Simulation)

Faculty
Aerospace Engineering
Copyright
© 2023 Sean Cowan
More Info
expand_more
Publication Year
2023
Language
English
Copyright
© 2023 Sean Cowan
Graduation Date
28-06-2023
Awarding Institution
Delft University of Technology
Programme
['Aerospace Engineering']
Faculty
Aerospace Engineering
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

While the space industry expands rapidly and space exploration becomes ever more relevant, this thesis aims to automate the design of Multiple Gravity-Assist (MGA) transfers using low-thrust propulsion. In particular, during the preliminary design phase of space missions, the combinatorial complexity of MGA sequencing is large and current optimisation approaches require extensive experience and can take days to simulate. Therefore, a novel optimisation approach is developed -- called the Recursive Target Body Approach -- that uses the hodographic-shaping low-thrust trajectory representation together with a combination of tree-search methods to automate the optimisation of MGA sequences. The approach gradually constructs the expected optimal MGA sequence by recursively evaluating the optimality of subsequent gravity-assist targets. Moreover, the approach includes novel figures of merit as well as parallelisation concepts to increase the robustness and accelerate the convergence. An Earth-Jupiter transfer with a maximum of three gravity assists is considered as a reference problem. Extensive tuning improved the quality of the MGA trajectory representations substantially and as a result, a robust low-thrust trajectory optimisation could be ensured. A distinct group of highly fit MGA sequences is consistently found that can be passed to a higher-fidelity method. In conclusion, the Recursive Target Body Approach can automatically and reliably be used for the preliminary optimisation of low-thrust MGA trajectories.

Files

License info not available
warning

File under embargo until 28-12-2025