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<p>In this paper, a data-driven topology optimization (TO) method is proposed for the efficient design of three-dimensional heat transfer structures. The presented method is composed of four parts. Firstly, the three-dimensional heat transfer topology optimization (HTTO) dataset, composed of both design parameters and the corresponding HTTO configuration, is established by the solid isotropic material with penalization (SIMP) method. Secondly, a high-performance surrogate model, named ResUNet-assisted generative adversarial nets (ResUNet-GAN), is developed by combining ReUNet and generative and adversarial nets (GAN). Thirdly, the same-resolution (SR) ResUNet-GAN is deployed to design three-dimensional heat transfer configurations by feeding design parameters. Finally, the finite element mesh of the optimized configuration is refined by the cross-resolution (CR) ResUNet-GAN to obtain near-optimal three-dimensional heat transfer configurations. Compared with conventional TO methods, the proposed method has two outstanding advantages: (1) the developed surrogate model establishes the end-to-end mapping from the design parameters to the three-dimensional configuration without any need for optimization iterations and finite element analysis; (2) both the SR ResUNet-GAN and the CR ResUNet-GAN can be employed individually or in combination to achieve each function, according to the needs of heat transfer structures. The data-driven method provides an efficient design framework for three-dimensional practical engineering problems.</p>

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Efficient Topology Optimization Design for Three-Dimensional Heat Transfer Structure Based on ResUNet-Involved Generative Adversarial Nets

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Jicheng Li,
  • Hongling Ye,
  • Nan Wei,
  • Yongjia Dong,
  • Sujun Wang

摘要

In this paper, a data-driven topology optimization (TO) method is proposed for the efficient design of three-dimensional heat transfer structures. The presented method is composed of four parts. Firstly, the three-dimensional heat transfer topology optimization (HTTO) dataset, composed of both design parameters and the corresponding HTTO configuration, is established by the solid isotropic material with penalization (SIMP) method. Secondly, a high-performance surrogate model, named ResUNet-assisted generative adversarial nets (ResUNet-GAN), is developed by combining ReUNet and generative and adversarial nets (GAN). Thirdly, the same-resolution (SR) ResUNet-GAN is deployed to design three-dimensional heat transfer configurations by feeding design parameters. Finally, the finite element mesh of the optimized configuration is refined by the cross-resolution (CR) ResUNet-GAN to obtain near-optimal three-dimensional heat transfer configurations. Compared with conventional TO methods, the proposed method has two outstanding advantages: (1) the developed surrogate model establishes the end-to-end mapping from the design parameters to the three-dimensional configuration without any need for optimization iterations and finite element analysis; (2) both the SR ResUNet-GAN and the CR ResUNet-GAN can be employed individually or in combination to achieve each function, according to the needs of heat transfer structures. The data-driven method provides an efficient design framework for three-dimensional practical engineering problems.