Abstract Current solar electric propulsion and launch vehicle technology allow sending multiple spacecraft to Mars simultaneously. For capture orbits with very high apoareion, the solar gravity perturbations deploy the spacecraft in vastly different areocentric orbits. This application requires knowledge of possible transfers and an efficient way to identify them; for example, a well-defined and easily accessed database of solutions. First, a method is developed to identify the required initial orbital elements and timing to target a final orbit, for a dynamical model ignoring the Martian eccentricity. This method can be applied to any planetary system with low heliocentric eccentricity. Second, the effect of Mars’ eccentricity and the corresponding time-dependence of solar distance are analyzed. This analysis requires a much larger number of numerical integrations. The research is therefore limited to placing bounds on the solution space. Third, this paper determines the way artificial neural networks can reduce the large number of required integrations by order(s) of magnitude while maintaining sufficient accuracy to enable preliminary transfer design. The neural networks enable the design of transfers between a low, near-polar orbit to Phobos and Deimos in an approximation of the eccentric model. These transfers are then validated in a higher fidelity ephemeris model.
Leveraging Artificial Neural Networks to Systematically Explore Solar Gravity Driven Transfers in the Martian System
The Journal of the Astronautical Sciences ; 66 , 3 ; 282-321
2019-03-25
40 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Circular hill problem , Eccentric hill problem , Solar gravitational perturbations , Machine learning , Artificial neural networks Engineering , Aerospace Technology and Astronautics , Mathematical Applications in the Physical Sciences , Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics)
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