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Structural and Multidisciplinary Optimization

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20.07.2020 | Research Paper

Topology optimization of hyperelastic structures using a modified evolutionary topology optimization method

verfasst von: Zeyu Zhang, Yong Zhao, Bingxiao Du, Xiaoqian Chen, Wen Yao

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2020

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Abstract

Soft materials are finding widespread implementation in a variety of applications, and it is necessary for the structural design of such soft materials to consider the large nonlinear deformations and hyperelastic material models to accurately predict their mechanical behavior. In this paper, we present an effective modified evolutionary topology optimization (M-ETO) method for the design of hyperelastic structures that undergo large deformations. The proposed M-ETO method is implemented by introducing the projection scheme into the evolutionary topology optimization (ETO) method. This improvement allows nonlinear topology optimization problems to be solved with a relatively big evolution rate, which significantly enhances the robustness. The minimal length scale is achieved as well. Numerical examples show that the proposed M-ETO method can stably obtain a series of optimized structures under different volume fractions with smooth boundaries. Moreover, compared with other smooth boundary methods, another merit of M-ETO is that the problem of the dependency on initial layout can be eliminated naturally due to the inherent characteristic of ETO.

Vorheriger Artikel High-risk prediction localization: evaluating the reliability of black box models for topology optimization

Nächster Artikel Topology optimization for three-dimensional design of segmented permanent magnet arrays

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Titel: Topology optimization of hyperelastic structures using a modified evolutionary topology optimization method
verfasst von: Zeyu Zhang
Yong Zhao
Bingxiao Du
Xiaoqian Chen
Wen Yao
Publikationsdatum: 20.07.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 6/2020
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-020-02654-9

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