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Topology optimization in OpenMDAO

  • Hayoung ChungEmail author
  • John T. Hwang
  • Justin S. Gray
  • H. Alicia Kim
Educational Article

Abstract

Recently, topology optimization has drawn interest from both industry and academia as the ideal design method for additive manufacturing. Topology optimization, however, has a high entry barrier as it requires substantial expertise and development effort. The typical numerical methods for topology optimization are tightly coupled with the corresponding computational mechanics method such as a finite element method and the algorithms are intrusive, requiring an extensive understanding. This paper presents a modular paradigm for topology optimization using OpenMDAO, an open-source computational framework for multidisciplinary design optimization. This provides more accessible topology optimization algorithms that can be non-intrusively modified and easily understood, making them suitable as educational and research tools. This also opens up further opportunities to explore topology optimization for multidisciplinary design problems. Two widely used topology optimization methods—the density-based and level-set methods—are formulated in this modular paradigm. It is demonstrated that the modular paradigm enhances the flexibility of the architecture, which is essential for extensibility.

Keywords

Topology optimization OpenMDAO Solid Isotropic Materials with Penalization (SIMP) Level-set Topology Optimization (LSTO) 

Notes

Funding information

This paper received support from the NASA Transformational Tools and Technologies Project, contract number NNX15AU22A.

Compliance with ethical standards

Replication of results

To comply with Replication of Results and help the reader in using the present work in research and education, the codes of the present work is open to public e codes (https://github.com/chungh6y/openmdao_TopOpt). Instruction for installation and running the program can be found therein.

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of California San DiegoSan DiegoUSA
  2. 2.NASA Glenn Research CenterClevelandUSA
  3. 3.Cardiff UniversityCardiffWales

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