39 lines
2.5 KiB
TeX
39 lines
2.5 KiB
TeX
\chapter{Conclusion} \label{conclusion}
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This thesis explored an approach for automating the initial analysis and discovery of useful
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interplanetary, low-thrust trajectories including the difficult task of optimizing the flyby
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parameters. This makes the mission designer's job significantly simpler in that they can
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simply explore a number of different flyby selection options in order to get a good
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understanding of the mission scope and search space for a given spacecraft, launch window,
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and target.
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In performing this examination, two results were selected for further analysis. These
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results are outlined in Table~\ref{results_table}. As can be seen in the table, both
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resulting trajectories have trade-offs in mission length, launch energy, fuel usage, and
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more. Each of these trajectories appear to be within the capabilities of existing launch
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vehicles in terms of $C_3$.
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In the course of producing this algorithm, a large number of improvement possibilities were
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noted. This work was based, in large part, on the work of Jacob Englander in a number of
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papers\cite{englander2014tuning}\cite{englander2017automated} \cite{englander2012automated}
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in which they explored the hybrid optimal control problem of multi-objective low-thrust
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interplanetary trajectories.
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In light of this, there are a number of additional approaches that Englander took in
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preparing their algorithm that were not implemented here in favor of reducing complexity and
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time constraints. For instance, many of the Englander papers explore the concept of an outer
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loop that utilizes a genetic algorithm to compare many different flyby planet choices
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against each other.
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Further improvements, in the name of performance stem from the field of computer science. An
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evolutionary algorithm such as the one proposed by Englander would benefit from high levels
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of parallelization. Therefore, it would be worth considering a GPU-accelerated or even
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cluster-computing capable implementation of the monotonic basin hopping algorithm.
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Finally, the monotonic basin hopping algorithm as currently written provides no guarantees
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of actual global optimization. Generally optimization is achieved by running the algorithm
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until it fails to produce newer, better trajectories for a sufficiently long time. But it
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would be worth investigating the robustness of the NLP solver as well as the robustness of
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the MBH algorithm basin drilling procedures in order to quantify the search granularity
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needed to completely traverse the search space.
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