Large-Scale Worst-Case Topology Optimization

Zhang, Di; Zhai, Xiaoya; Fu, Xiao-Ming; Wang, Heming; Liu, Ligang

Large-Scale Worst-Case Topology Optimization

Files

v41i7pp529-540.pdf (80.85 MB)

paper1054_code.zip (3.95 MB)

Date

2022

Authors

Zhang, Di
Zhai, Xiaoya
Fu, Xiao-Ming
Wang, Heming
Liu, Ligang

Publisher

The Eurographics Association and John Wiley & Sons Ltd.

Abstract

We propose a novel topology optimization method to efficiently minimize the maximum compliance for a high-resolution model bearing uncertain external loads. Central to this approach is a modified power method that can quickly compute the maximum eigenvalue to evaluate the worst-case compliance, enabling our method to be suitable for large-scale topology optimization. After obtaining the worst-case compliance, we use the adjoint variable method to perform the sensitivity analysis for updating the density variables. By iteratively computing the worst-case compliance, performing the sensitivity analysis, and updating the density variables, our algorithm achieves the optimized models with high efficiency. The capability and feasibility of our approach are demonstrated over various large-scale models. Typically, for a model of size 512×170×170 and 69934 loading nodes, our method took about 50 minutes on a desktop computer with an NVIDIA GTX 1080Ti graphics card with 11 GB memory.

        @article{10.1111:cgf.14698
,
journal = {Computer Graphics Forum},
title = {{Large-Scale Worst-Case Topology Optimization
}},
author = {Zhang, Di and 
Zhai, Xiaoya and 
Fu, Xiao-Ming and 
Wang, Heming and 
Liu, Ligang
},
year = {2022
},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.14698
}
}