4.7 Article

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

Journal

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 59, Issue 4, Pages 1075-1104

Publisher

SPRINGER
DOI: 10.1007/s00158-019-02211-z

Keywords

Multidisciplinary design optimization; Coupled systems; Complex systems; Sensitivity analysis; Derivative computation; Adjoint methods; Python

Funding

  1. NASA ARMD Transformational Tools and Technologies project
  2. National Science Foundation [1435188]
  3. Directorate For Engineering
  4. Div Of Civil, Mechanical, & Manufact Inn [1435188] Funding Source: National Science Foundation

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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.

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