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

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. F...

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Vydáno v:Structural and multidisciplinary optimization Ročník 59; číslo 4; s. 1075 - 1104
Hlavní autoři: Gray, Justin S., Hwang, John T., Martins, Joaquim R. R. A., Moore, Kenneth T., Naylor, Bret A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019
Springer Nature B.V
Témata:
Algorithms
Computational Mathematics and Numerical Analysis
Design analysis
Design optimization
Engineering
Engineering Design
Multidisciplinary design optimization
Numerical models
Open source software
Research Paper
Theoretical and Applied Mechanics
Topology optimization
Trajectory optimization
Wing design
Derivative computation
Coupled systems
Adjoint methods
Sensitivity analysis
Complex systems
Multidisciplinary design optimization
Python
ISSN:1615-147X, 1615-1488
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Shrnutí: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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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-019-02211-z