OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization

TLDR

Multidisciplinary design optimization tackles coupled numerical models of complex engineering systems, yet existing software lacks efficient algorithms and derivative computation needed for large‑scale gradient‑based optimization. This paper introduces OpenMDAO, an open‑source framework that applies Newton‑type algorithms and hierarchical strategies to efficiently solve coupled systems. OpenMDAO computes coupled derivatives efficiently by exploiting problem sparsity and hierarchical structure, enabling high computational performance. Benchmarks on scalable test problems and applications in trajectory optimization, wing design, and structural topology optimization demonstrate OpenMDAO’s effectiveness in coupling existing models and building new multidisciplinary models.

Abstract

Multidisciplinary design optimization (MDO) is concerned with solving design problems involving coupled numerical models of complex engineering systems. While various MDO software frameworks exist, none of them take full advantage of state-of-the-art algorithms to solve coupled models efficiently. Furthermore, there is a need to facilitate the computation of the derivatives of these coupled models for use with gradient-based optimization algorithms to enable design with respect to large numbers of variables. In this paper, we present the theory and architecture of OpenMDAO, an open-source MDO framework that uses Newton-type algorithms to solve coupled systems and exploits problem structure through new hierarchical strategies to achieve high computational efficiency. OpenMDAO also provides a framework for computing coupled derivatives efficiently and in a way that exploits problem sparsity. We demonstrate the framework's efficiency by benchmarking scalable test problems. We also summarize a number of OpenMDAO applications previously reported in the literature, which include trajectory optimization, wing design, and structural topology optimization, demonstrating that the framework is effective in both coupling existing models and developing new multidisciplinary models from the ground up. Given the potential of the OpenMDAO framework, we expect the number of users and developers to continue growing, enabling even more diverse applications in engineering analysis and design.