<p>In structural topology optimization, many elements evolve into solid states during the early and middle stages of the process. However, their matrices remain integrated into the global stiffness matrix, contributing to computational cost during each optimization iteration. To address this issue, this study proposes an adaptive elements aggregation method. This method identifies elements confirmed as solid and aggregates them to reduce the dimensions of the global stiffness matrix during the optimization process, thereby improving computational efficiency. The proposed approach is combined with Floating Projection Topology Optimization (FPTO), which ensures optimized designs with significantly fewer intermediate-density elements. The method is applied to both stiffness and stress optimization in this study. The results demonstrate that the adaptive elements aggregation method is effective, reducing computational time per iteration while maintaining relative optimization accuracy.</p>

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Topology optimization with an adaptive elements aggregation method

  • Shi-An Zhou,
  • Song Yao

摘要

In structural topology optimization, many elements evolve into solid states during the early and middle stages of the process. However, their matrices remain integrated into the global stiffness matrix, contributing to computational cost during each optimization iteration. To address this issue, this study proposes an adaptive elements aggregation method. This method identifies elements confirmed as solid and aggregates them to reduce the dimensions of the global stiffness matrix during the optimization process, thereby improving computational efficiency. The proposed approach is combined with Floating Projection Topology Optimization (FPTO), which ensures optimized designs with significantly fewer intermediate-density elements. The method is applied to both stiffness and stress optimization in this study. The results demonstrate that the adaptive elements aggregation method is effective, reducing computational time per iteration while maintaining relative optimization accuracy.