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Article overview
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Data-driven Topology Optimization of Channel Flow Problems | | Ce Guan
; Jianyu Zhang
; Zhen Li
; Yongbo Deng
; | | Date: |
1 Sep 2023 | | Abstract: | Typical topology optimization methods require complex iterative calculations,
which cannot be realized in meeting the requirements of fast computing
applications. The neural network is studied to reduce the time of computing the
optimization result, however, the data-driven method for fluid topology
optimization is less of discussion. This paper intends to introduce a neural
network architecture that avoids time-consuming iterative processes and has a
strong generalization ability for topology optimization for Stokes flow.
Different neural network methods that have been already successfully used in
solid structure optimization problems are mutated and examined for fluid
topology optimization cases, including Convolution Neural Networks (CNN),
conditional Generative Adversarial Networks (cGAN), and Denoising Diffusion
Implicit Models (DDIM). The presented neural network method is tested on the
channel flow topology optimization problems for Stokes flow. The results have
shown that our presented method has high pixel accuracy, and we gain a 663
times decrease in execution time compared with the conventional method on
average. | | Source: | arXiv, 2309.00278 | | Services: | Forum | Review | PDF | Favorites | |
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