Modeling the operations of a realistic CubeSat using Model-Based Systems Engineering (MBSE)
Model-Based Systems Engineering (MBSE) is emerging in the space systems community to enable the analysis, design and optimization of current and future missions. MBSE uses analytical-based models to capture, communicate, verify and execute space system designs and is designed to replace conventional document-driven approaches to systems engineering. Toward this goal, the Space Systems Working Group (SSWG) Challenge Team has been working on MBSE efforts over the past several years. We are sponsored by the International Council on Systems Engineering (INCOSE) and the team has consisted of members from industry (NASA's Jet Propulsion Lab, Phoenix Integration, InterCAX) and academia (MIT, Georgia Tech, University of Michigan). PAST Last year, we presented a paper at the 2012 IEEE Aerospace Conference that developed an MBSE framework (generic template that could be applied to model a variety of systems) for a CubeSat, which is a type of miniaturized satellite. PRESENT In our 2013 IEEE Aerospace Conference paper, being presented next week in Big Sky, Montana, we have demonstrated applicability of the framework to capture a realistic CubeSat mission, the Radio Aurora Explorer (RAX). In particular, we’ve integrated diverse software that is conventionally used by space systems engineers, including MagicDraw, Cameo Simulation Toolkit, STK from AGI, MATLAB, ParaMagic from InterCAX and PHX ModelCenter, to enable the execution of operational mission scenarios. Using the integrated simulation environment, we’ve performed rapid systems engineering analysis and trades. We are excited about this modeling and simulation framework because it will improve approaches used to design and simulate missions for use in the classroom, for research applications and for realistic mission development and operations. FUTURE For future space missions, such as constellation and interplanetary architectures, the design space is broad and challenging to explore because these multi-satellite missions have complex, and often competing, objectives/constraints, and must operate in poorly understood/stochastic environments. The MBSE approaches we're developing will enable mission modeling, analysis, scenario execution, verification and validation of mission requirements, optimization and sensitivity analysis for these challenging future mission concepts, which may have been impossible or difficult with conventional approaches. We are excited that our our work is advancing the MBSE modeling and simulation capabilities to enable the design, analysis and optimization of future novel mission architectures and address the need for this capability in the space systems community!