Evolutionary Computing for the Design Search and Optimization of Space Vehicle Power Subsystems

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Our preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates. Mark Kordon, Gerhard Klimeck, David Hanks Oct. 7,2003 Evolutionary Computing for the Design Search and Optimization of Space Vehicle Power Subsystems Mark Kordon Gerhard Klimeck Jet Propulsion Laboratory 4800 Oak Grove Drive Pasadena, CA 9 100 1 Jet Propulsion Laboratory 4800 Oak Grove Drive Pasadena, CA 9 100 1 [email protected] gekco@iuI .nasa. gov 8 18-393-0476 8 18-354-2 180 AbstractEvolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Our preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates. Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Our preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.