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Stable Explicit Topology Optimization Method with Geometric Nonlinearity
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DOI：10.1007/s10338-026-00725-w.png)
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Abstract
En 中文
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.
Keywords:
Moving morphable components
Geometric nonlinearity
Topology optimization