Multidisciplinary Design Optimization: Architecture Review and Implementation

Multidisciplinary Design Optimization (MDO) focuses on optimization problems spanning multiple engineering disciplines that are commonly encountered in the engineering design process of complex systems. To solve a MDO problem requiresmanaging disciplinary coupling and balancing resource-intensive disciplinary analyses of individual subsystems to achieve an optimal design at the system-level. In this paper the basics of MDO are presented in Section 1 followed by a review of four MDO architectures in Section 2 including: All-At Once (AAO), Simultaneous Analysis and Design (SAND), Individual Disciplinary Feasible (IDF), and Collaborative Optimization (CO). In Section 3, two example problems - a geometric programming problem, and a propane combustion problem - are presented and reformulated to fit the four MDO architectures. Each problem formulation was implemented using python and solved. The paper concludes with a simple benchmarking analysis comparing example problems and architecture implementations across solution precisionand the number of function calls to the disciplinary analysis. The findings of this architecture review and implementation highlight the importance of tailoring the MDO architecture decision to the specific MDO problem characteristics in order to optimize the design process effectively.