SPACE LOGISTICS OPTIMIZATION TOOL: Development of a Space Logistics Optimization Tool to Support Decision-Making for Technology Road Mapping

This thesis proposes a new methodology to provide compromises between different spacecraft design and space mission architectures, particularly for complex missions. Therefore, the effectiveness of the novel approach is assessed through a case study regarding the delivery of a payload on the lunar surface. As space missions become more complex and long-term, there is a more significant need for an effective method of designing and optimizing these complex missions. While most traditional mission design studies focus on spacecraft trajectories, recent research on space logistics has introduced rigorous mathematical optimization in the design of multi-mission space campaigns to try to automate the mission design. However, these existing methods aim at low-fidelity and high-level design, while almost completely neglecting the spacecraft design resulting from the requirements imposed by the simulated mission. In light of that, this document proposes a new optimization methodology in the design of space logistics missions with design trade-offs to increase the applicability and flexibility of space logistics optimization methods. Part of the tool developed in the frame of this thesis was already in possession of EPFL. The tool has been then adapted and improved in order to extend its applicability from low Earth orbits to the Cislunar space. To do this, all the peculiarities of this environment and the differences in the design requirements of the satellites have been considered, in order to obtain the most accurate results. The program was then tested by modelling the Apollo 11 mission, of which a great amount of data is freely available, in order to validate the mass calculation models. Finally, a use case in the Cislunar trade space is used to prove the effectiveness of the proposed method. An important result of this case study is the demonstration that the proposed approach allows to obtain plausible and useful results for carrying out trade-offs in the initial design phases of a multi-mission space program, even if the simulation is mostly based on linear models.