Multiscale structural optimization for prescribed deformations in the nonlinear elastic regime
File(s)
Published version
Author(s)
Murphy, ryan
Thillaithevan, dilaksan
Hewson, Robert
Santer, Matthew
Type
Journal Article
Abstract
In this paper, a multiscale structural optimization framework capable of efficiently designing two-scale structures with prescribed displacements in the nonlinear elastic regime is presented. In contrast to previous multiscale structural optimization frameworks, which are founded upon the assumptions of linear elasticity, the present framework is capable of efficiently operating within the nonlinear elastic regime. At the core of the present framework is a parameterized microscale geometry, which through the straightforward manipulation of the microscale parameters provides direct access to both positive and negative Poisson’s ratios. The microscale model is concurrently coupled to the macroscale model such that only the microscale parameter space traversed by the optimizer is resolved during the optimization procedure, leading to a significant reduction in the computational expense of analysis. To demonstrate the capability of this framework, three prescribed deformation profiles are targeted by three distinct optimization procedures. In all instances, the deformation profile is successfully targeted. To verify the accuracy of the optimized structures, high-fidelity single-scale simulations are performed. In each case, excellent agreement is noted between the high-fidelity simulations and the corresponding optimized macroscale displacement fields, with errors of less than 10%.
Date Issued
2024-12
Date Acceptance
2024-08-30
Citation
Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems, 2024, 67 (12)
ISSN
1615-147X
Publisher
Springer
Journal / Book Title
Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems
Volume
67
Issue
12
Copyright Statement
© The Author(s) 2024 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://link.springer.com/article/10.1007/s00158-024-03881-0
Publication Status
Published
Article Number
202
Date Publish Online
2024-11-27