Multi-mission sizing and selection methodology for space habitat subsystems

Abstract

Future space missions aim to set up exploration missions in further space and establish settlements on other celestial bodies like the Moon or Mars. In this context, subsystem sizing and selection is crucial, not only because resource management is critical for the astronauts’ survival, but also because subsystems can account for more than 20% of the total mass of the habitat, so reducing their size can greatly impact the cost of the mission. A few tools already exist to size space habitat subsystems and assess their performance. However, these tools are either very high-fidelity and very slow or instantaneous but steady-state. Steady-state tools do not allow to take risks or mission variations into account and the dynamic, slower tools are less performing at helping stakeholders evaluate the impact of technology trade-offs because of their long running time. Faster sizing tools would also allow to implement additional capabilities, such as multi-mission sizing, which could be used to develop lunar or martian settlements. These tools are also used in the context of point-based design, which focuses on the development of one design throughout the process. Such approach can lead to a sub-optimal design because the selection of an alternative is made early in the design process, based on low-fidelity analyses. In addition, because the costs and design choices are committed early in the design process, requirements or design changes can have very significant cost consequences. This research proposes a new sizing capability, developed using HabNet [1], a dynamic space habitat simulation tool. It is faster than existing dynamic sizing tools and it allowed to develop a multi-mission sizing methodology using Design Space Exploration. Finally, leveraging the faster sizing tool developed to create surrogate models for the size of the elements in the habitat, it was shown that trade-off analyses can be used to support set-based design during the conceptual design phase. Consequently, the methodology proposed is faster than what is currently used to size and select space habitat subsystem technologies. It gives more insight to the user because it can perform instantaneous trade-offs. However, the quality of the surrogate models generated is not sufficient to validate the multi-mission sizing method and environment developed during this thesis. This methodology could be used as a basis for the development of a set-based design method for space habitats. Numerous capabilities, including the evaluation of the impact of disruptions or the level of uncertainty associated with the various alternatives considered, could be easily implemented and added to the existing tool.