Unifying Monolithic Architectures for Large-Scale System Design Optimization

Abstract

Large-scale system design optimization is a numerical technique used in solving system design problems that involve a large number of design variables. These systems are often multidisciplinary, with many disciplines interacting with each other. The scale of these problems demands a gradient-based approach for efficient solutions, and it is often implemented by coupling an engineering model with an optimizer. A recently developed theory on multidisciplinary derivative computation has made it feasible to solve large-scale system design optimization problems in only hundreds of model evaluations. This has led to an increase in the number of applications for large-scale system design optimization with new applications still emerging. This paper presents a new optimization formulation that can further reduce the required number of model evaluations by unifying two widely used optimization architectures, namely, multidisciplinary feasible, and simultaneous analysis and design. Complex engineering systems that require solutions of large nonlinear systems can potentially benefit from this new formulation, and the optimized solutions can be reached in just tens of equivalent model evaluations. We demonstrate this order of magnitude improvement using a bar design problem. The paper also provides details on the practical implementation of this new formulation in an equality-constrained optimization setting.