Multidisciplinary design and optimization of intelligent Distributed Satellite Systems for EARTH observation

Abstract

Recent advances in small, connected and intelligent satellite systems have created a wide range of opportunities for the adoption of intelligent Distributed Satellite Systems (iDSS) in communication, navigation and Earth Observation (EO) missions. iDSS are goal-oriented systems comprising of multiple satellites or modules that interact, communicate and/or cooperate with each other to accomplish the desired mission goals. The ability to mass-produce low-cost small satellites and contemporary developments in avionics/astrionics technology have spurred interest in iDSS, especially for Low Earth Orbit (LEO) satellite constellations and regional clusters. The SmartSat Cooperative Research Centre (CRC) and Australian space roadmap, as well as the landmark National Space Programme strategy and priorities, encompass EO. To date, insufficient progress and no conclusive outcome was made in terms of how contemporary Multidisciplinary Design Optimization (MDO) models and tools can be best tailored to the new capabilities and specificities of iDSS. The MDO of iDSS is challenging because it introduces new variables and highly non-linear interactions. In this context, we propose an MDO methodology to optimize an iDSS for persistent coverage over the entire Australian landmass. Several aspects of the iDSS are considered in this work, including the constellation model, subsystem models and the coupling interactions between different satellite subsystems and constellation design parameters. The constellation configuration, as well as the subsystems, are modelled using OpenMDAO, which is used to analyze and visualize the planned iDSS EO mission. The iDSS is then optimized using the Multidisciplinary Feasible (MDF) architecture approach and the iDSS interdependencies are numerically treated using the Nonlinear Block Gauss-Seidel (NLBGS) iterative solver. The resulting N2 diagrams are presented and the proposed solution is both spatially and temporally optimized, demonstrating that the proposed iDSS will enable near real-time persistent coverage over the entire Australian continent.