Rapid model-based inter-disciplinary design of a CubeSat mission

With an increase in the use of small, modular, resource-limited satellites for Earth orbiting applications, the benefit to be had from a model-based architecture that rapidly searches the mission trade-space and identifies near-optimal designs is greater than ever. This work presents an architecture that identifies trends between conflicting objectives (e.g. lifecycle cost and performance) and decision variables (e.g. orbit altitude and inclination) such that informed assessment can be made as to which design/s to take on for further analysis. The models within the architecture exploit analytic methods where possible, in order avoid computationally expensive numerical propagation, and achieve rapid convergence. Two mission cases are studied; the first is an Earth observation satellite and presents a trade-off between ground sample distance and revisit time over a ground target, given altitude as the decision variable. The second is a satellite with a generic scientific payload and shows a more involved trade-off, between data return to a ground station and cost of the mission, given variations in the orbit altitude, inclination and ground station latitude. Results of each case are presented graphically and it is clear that non-intuitive results are captured that would typically be missed using traditional, point-design methods, where only discrete scenarios are examined.