A methodology for conducting design trades related to advanced in-space assembly
Jara de Carvalho Vale de Almeida, Lourenco
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In the decades since the end of the Apollo program, manned space missions have been confined to Low Earth Orbit. Today, ambitious efforts are underway to return astronauts to the surface of the Moon, and eventually reach Mars. Technical challenges and dangers to crew health and well-being will require innovative solutions. The use of In-Space Assembly (ISA) can provide critical new capabilities, by freeing designs from the size limitations of launch vehicles. ISA can be performed using different strategies. The current state-of-the-art strategy is to dock large modules together. Future technologies, such as welding in space, will unlock more advanced strategies. Advanced assembly strategies deliver smaller component pieces to orbit in highly efficient packaging but require lengthy assembly tasks to be performed in space. The choice of assembly strategy impacts the cost and duration of the entire mission. As a rule, simpler strategies require more deliveries, increasing costs, while advanced strategies require more assembly tasks, increasing time. The effects of these design choices must be modeled in order to conduct design trades. A methodology to conduct these design trades is presented. It uses a model of the logistics involved in assembling a space system, including deliveries and assembly tasks. The model employs a network formulation, where the pieces of a structure must flow from their initial state to a final assembly state, via arcs representing deliveries and assembly tasks. By comparing solutions obtained under different scenarios, additional design trades can be performed. This methodology is applied to the case of an Artificial Gravity Space Station. Results for the assembly of this system are obtained for a baseline scenario and compared with results after varying parameters such as the delivery and storage capacity. The comparison reveals the sensitivities of the assembly process to each parameter and the benefits that can be gained from certain improvements, demonstrating the effectiveness of the methodology.
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