Scholarlink

<p>The consideration of geometric nonlinearity is essential for accurately predicting structural responses under a sufficiently large load. To achieve topology optimization for structures experiencing significant deformation, several strategies have been proposed to address the challenge of structural analysis failure due to the distortion of low-density material. Since computational frameworks often suffer from stability issues such as simulation failure and iterative divergence under high loads, most research has focused on geometric nonlinearity at relatively low load levels, with only a few attempts to optimize structures subjected to very large loads. In such scenarios, sophisticated parameter evolution strategies must be meticulously designed to prevent analysis failure. This article establishes a more stable and efficient topology optimization framework for designing structures considering geometric nonlinearity based on the stabilized time-series moving morphable components (STSMMC) method. The introduced geometry interpolation aims to eliminate analysis failure at the element level. Additionally, the STSMMC method addresses the failure issues at the structure level (e.g., buckling) through the failure-rejection trust region-based moving asymptotes algorithm. Numerical examples illustrate that the proposed approach works stably in optimizing structures under large loads.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stable Explicit Topology Optimization Method with Geometric Nonlinearity

  • 期刊论文

  • 发表日期：

    2026年3月10日

查看全文
  • Xueyan Hu,
  • Zonghao Li,
  • Ronghao Bao,
  • Erasmo Carrera,
  • Weiqiu Chen

摘要

The consideration of geometric nonlinearity is essential for accurately predicting structural responses under a sufficiently large load. To achieve topology optimization for structures experiencing significant deformation, several strategies have been proposed to address the challenge of structural analysis failure due to the distortion of low-density material. Since computational frameworks often suffer from stability issues such as simulation failure and iterative divergence under high loads, most research has focused on geometric nonlinearity at relatively low load levels, with only a few attempts to optimize structures subjected to very large loads. In such scenarios, sophisticated parameter evolution strategies must be meticulously designed to prevent analysis failure. This article establishes a more stable and efficient topology optimization framework for designing structures considering geometric nonlinearity based on the stabilized time-series moving morphable components (STSMMC) method. The introduced geometry interpolation aims to eliminate analysis failure at the element level. Additionally, the STSMMC method addresses the failure issues at the structure level (e.g., buckling) through the failure-rejection trust region-based moving asymptotes algorithm. Numerical examples illustrate that the proposed approach works stably in optimizing structures under large loads.