Quantification of flyby effects in the three-body problem using the Gaussian process method

Abstract

The gravity assist (GA) plays an important role in space missions since itwas first applied by the Luna 3 vehicle in 1959. For preliminary trajectory design, the so-called patchedconics model provides a simple model for a gravity assist. This approach, based on twobody formulations, splits amulti-body probleminto a succession of two-body problems. This model has a fundamental assumption: the trajectory of the spacecraft is driven by one celestial body only. A boundary for switching the driving bodies is defined by the Sphere of Influence (SoI) of the GA body. The patched conics model cannot be used to study low-energy trajectories. Moreover, it fails to describe special dynamics existing in the multi-body regime, such as the invariant manifolds. The three-body formulation is a logical choice to study the dynamics in the multi-body problem. In order to reduce its inherent difficulty, the circular restricted three-body problem (CR3BP) formulation is developed to study the behavior of the motion of a particle influenced by two massive bodies simultaneously. Flybys in the CR3BP have been studied by many researchers, using a numerical or semi-analytical approach, e.g. the Flyby map (FM) and Keplerianmap (KM), respectively. Inspired by these approaches and the idea of artificial intelligence, this thesis focuses on the investigation of flybys froma machine-learning perspective.