Yihui Wang1, Qishi Li1, Wei Sha1, Mi Xiao1,*, Liang Gao1
The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012356
Abstract Heat conduction structures are widely employed in thermal management of electronic components across aerospace, electronics, and related domains, ensuring sustained operational performance and longevity. However, conventional approaches to heat conduction structure design are encumbered by constraints on design flexibility, suboptimal thermal dissipation characteristics, and inefficiencies. Addressing these limitations, this study presents a novel approach leveraging deep learning for the design of anisotropic heat conduction structures. Initially, a pre-trained deep generative model is deployed to enable real-time generation of topologically functional cell (TFC) at the microscale. With the introduction of rotation angles of each TFC, these More >
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