🤖 AI Summary
Traditional satellite design relies on conservative safety margins to account for uncertainties, often resulting in substantial performance penalties. This work proposes an integrated framework that combines uncertainty quantification, sensitivity analysis, and reliability-based multidisciplinary design optimization, applied for the first time to an industrial-scale dual-communication rideshare satellite system. By eliminating conventional safety margins and instead ensuring that all design constraints are satisfied with high probability, the proposed approach reduces performance loss by 66%. The framework demonstrates strong scalability and is readily extendable to the collaborative optimization of satellite constellations comprising any number of spacecraft.
📝 Abstract
In satellite design, it is common practice to add safety margins to the constraints to achieve conservative solutions that are robust to uncertainties. This robustness often comes at the expense of performance and it may be more appropriate to include uncertainties in the definition of the design problem. This work addresses such a challenge by applying techniques of multidisciplinary design optimization under uncertainty to an industrial use case. The latter is a pair of telecommunication satellites launched together in a stacked configuration. Each satellite is a strongly coupled multidisciplinary system while the two satellites are not coupled at all. This use case can be extended to an arbitrary number of satellites. By considering uncertainty quantification techniques such as sensitivity analysis and reliability-based design optimization, this study demonstrates that accounting for uncertainties in the design problem results in a 66% reduction in performance loss compared to the adding of a predefined safety margins, while guaranteeing the feasibility of constraints with high probability.