The Jovian system is one of the most attractive destinations for scientific space exploration, with many missions having flown to Jupiter and its moons. Among the latter, Io stands out due to its intense volcanic activity, with plumes extending up to hundreds of kilometers above its surface. Scientists believe that sampling, returning, and analyzing the particles ejected from Io’s volcanoes has the potential to unlock valuable information about Earth’s formation, and the history of the Solar System. For this reason, the concept of an Io Sample Return mission was born at the Jet Propulsion Laboratory, California Institute of Technology, where this thesis was carried out.
The project focuses on the mission design and trajectory optimization of a Discovery-class Io Sample Return concept. It investigates which geometry, maneuvers sequence, and flyby trajectories, can enable the sampling of Io’s Prometheus plume through a single flyby, before returning the material back to Earth.
Firstly, a broad-search of feasible patched-conics round-trip trajectories to Jupiter is conducted, using a simplified two-bodies model. The search incorporates launch, entry, time of flight, mission delta-V, and Io encounter constraints. Two algorithms for the reconstruction of ballistic and targeted flybys are developed and integrated within the trajectories-search workflow. This phase highlights the infeasibility of the 14 years flight time constraint and the 8 km/s maximum Io-relative speed during sampling. The two thresholds are increased to 18 years and 10 km/s, respectively. The solutions-space is progressively filtered with the aid of a primer-vector optimizer, and a single candidate trajectory is chosen for further studies. The solutions from the broad-search are also used to conduct a sensitivity study on alternative plume targets, which highlights Prometheus’ ideal position for sampling missions that avoid Jupiter orbit insertion.
The selected patched-conics candidate is used as initial guess for the numerical propagation and optimization of the end-to-end mission. A high-level trade-off is conducted to select the most suitable approach to propagate the trajectory. Two optimization approaches are then compared. An arc-wise scheme, in which each interplanetary transfer is optimized individually, and an all-arcs method, where the Earth-Jupiter and Jupiter-Earth journeys are each optimized in their entirety. Self-Adaptive Differential Evolution (SADE) and Generational Multi-Objective Evolutionary Algorithm by Decomposition (GMOEA/D) are the two optimizers of choice.
Optimization runs conducted using the patched-conics initial guess prove unable to converge to acceptable solutions. Moreover, single-objective optimization struggles to satisfy position discontinuities requirements, when adopting the all-arcs approach. The patched-conics solution is therefore extended to multi-conic, leading to significant performance improvements. Final results show that GMOEA/D outperforms SADE using the all-arcs approach, and finds a solution that satisfies delta-V, launch C3, entry speed, and position discontinuities constraints. It also improves the total delta-V of the baseline multi-conic solution by about 70 m/s, leaving over 700 m/s of margin on the mission delta-V budget.