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<p>Stress-based topology optimization and nonlinear structural topology optimization are gaining increasing attention in order to make topology optimization more realistic. The aim of this work is to propose a method for the stress-based topology optimization to minimize the <i>p</i>-norm stress subject to the volume constraints of geometrically nonlinear structures within the framework of the extended bi-directional evolutionary structural optimization method. The <i>p</i>-norm stress aggregation approach is adopted for the measure of global stress level. A computationally efficient sensitivity number formulation for stress-based geometrically nonlinear structures is derived from the adjoint sensitivity of the global stress measure. Both topology variables and sensitivity numbers are filtered to stabilize the optimization procedure. And the filtered sensitivity numbers are further stabilized with their historical information. A series of comparison studies have been conducted to validate the effectiveness of the method on several benchmark design problems.</p>

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Stress-Based Topology Optimization of Geometrically Nonlinear Structures

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Yongsheng Han,
  • Runze Zhou,
  • Linfeng Han

摘要

Stress-based topology optimization and nonlinear structural topology optimization are gaining increasing attention in order to make topology optimization more realistic. The aim of this work is to propose a method for the stress-based topology optimization to minimize the p-norm stress subject to the volume constraints of geometrically nonlinear structures within the framework of the extended bi-directional evolutionary structural optimization method. The p-norm stress aggregation approach is adopted for the measure of global stress level. A computationally efficient sensitivity number formulation for stress-based geometrically nonlinear structures is derived from the adjoint sensitivity of the global stress measure. Both topology variables and sensitivity numbers are filtered to stabilize the optimization procedure. And the filtered sensitivity numbers are further stabilized with their historical information. A series of comparison studies have been conducted to validate the effectiveness of the method on several benchmark design problems.