Automated Multiple Gravity-Assist Sequence Optimisation

An intelligent parallel-computing methodology

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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.