Large-Scale Multidisciplinary Optimization of a Small Satellite’s Design and Operation

Abstract

The design of satellites and their operation is a complex task that involves a large number of variables and multiple engineering disciplines. Thus, it could benefit from the application of multidisciplinary design optimization, but previous efforts have been hindered by the complexity of the modeling and implementation, discontinuities in the design space, and the wide range of time scales. We address these issues by applying a new mathematical framework for gradient-based multidisciplinary optimization that automatically computes the coupled derivatives of the multidisciplinary system via a generalized form of the adjoint method. The modeled disciplines are orbit dynamics, attitude dynamics, cell illumination, temperature, solar power, energy storage, and communication. Many of these disciplines include functions with discontinuities and nonsmooth regions that are addressed to enable a numerically exact computation of the derivatives for all of the modeled variables. The wide-ranging time scales in the design problem, spanning 30 s to one year, are captured through a combination of multipoint optimization and the use of a small time step in the analyses. Optimizations involving over 25,000 design variables and 2.2 million state variables require 100 h to converge three and five orders of magnitude in optimality and feasibility, respectively. The results show that the geometric design variables yield a 40% improvement in the total data downloaded, which is the objective function, and the operational design variables yield another 40% improvement. This demonstrates not only the value in this approach for the design of satellites and their operation, but also promise for its application to the design of other large-scale engineering systems.