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

By Justin S. Gray, John T. Hwang, Joaquim R. Martins, K. T. Moore, Bret A. Naylor. Structural And Multidisciplinary Optimization, 2019

610 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7d03a198264330cb0752488a78ac83c79a73cc5d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Gaetan K. W. Kenway
2. Charles A. Mader
3. Ping He
4. Joaquim R. Martins
5. Andrew B. Lambe
6. Martin P. Bendsøe

In Global and Planetary Change

1. Charles A. Mader
2. Ping He
3. Joaquim R. Martins
4. Gaetan K. W. Kenway

In Statistics and Probability

1. Gaetan K. W. Kenway
2. Charles A. Mader
3. Ping He
4. Joaquim R. Martins
5. Martin P. Bendsøe

Citations in the corpus

• Effective adjoint approaches for computational fluid dynamics (2019, methodology): “This theoretical development resulted in the development of an algorithmic framework for the solution and derivative computation of coupled systems called modular analysis and unified derivatives [118], which has since been implemented in the OpenMDAO framework [119].”