Version 1
: Received: 6 October 2022 / Approved: 7 October 2022 / Online: 7 October 2022 (07:32:06 CEST)
How to cite:
He, J.; Abueidda, D. Multiphysics Topology Optimization Using Automatic Differentiation and Adjoint Method. Preprints2022, 2022100071. https://doi.org/10.20944/preprints202210.0071.v1
He, J.; Abueidda, D. Multiphysics Topology Optimization Using Automatic Differentiation and Adjoint Method. Preprints 2022, 2022100071. https://doi.org/10.20944/preprints202210.0071.v1
He, J.; Abueidda, D. Multiphysics Topology Optimization Using Automatic Differentiation and Adjoint Method. Preprints2022, 2022100071. https://doi.org/10.20944/preprints202210.0071.v1
APA Style
He, J., & Abueidda, D. (2022). Multiphysics Topology Optimization Using Automatic Differentiation and Adjoint Method. Preprints. https://doi.org/10.20944/preprints202210.0071.v1
Chicago/Turabian Style
He, J. and Diab Abueidda. 2022 "Multiphysics Topology Optimization Using Automatic Differentiation and Adjoint Method" Preprints. https://doi.org/10.20944/preprints202210.0071.v1
Abstract
An essential step in solving any topology optimization problem is determining the sensitivities of the objective function and optimization constraints. Unfortunately, these sensitivities are usually derived and implemented manually. Nontrivial objective functions and constraints, especially with the involvement of material and geometric nonlinearities, need strenuous mathematical derivation, leading to error-prone implementation. Another intriguing approach to finding sensitivities is automatic differentiation. This paper uses the automatic differentiation and adjoint method to find the sensitivities for two multiphysics topology optimization problems: 1) thermoelasticity and 2) piezoelectricity. This approach is not limited to these examples and can be easily extended to other single- or multi-physics topology optimization problems.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
