A machine learning-based generative design approach for rapid topology optimization of microchannel heat sinks

Zhipeng Yang, Shaopeng He, Jiacheng Yu, Qianglong Wang, Hanrui Qiu, Mingjun Wang, Wenxi Tian, G.H. Su,
A machine learning-based generative design approach for rapid topology optimization of microchannel heat sinks,
International Communications in Heat and Mass Transfer,
Volume 169, Part B,
2025,
109655,
ISSN 0735-1933,
https://doi.org/10.1016/j.icheatmasstransfer.2025.109655.
(https://www.sciencedirect.com/science/article/pii/S0735193325010814)
Abstract: Microchannel heat sinks are crucial for managing thermal loads in high-power systems, yet conventional topology optimization methods are computationally prohibitive for real-time applications. Furthermore, existing machine learning-based approaches lack adaptability to constraints and user-defined requirements. To address these issues, we propose a machine learning-assisted generative design framework that enables real-time, high-fidelity topology optimization, enhancing efficiency and practicality in engineering scenarios. Key findings include: (1) Multi-objective weighting governs the trade-off between topology and performance. Adjusting the weights for heat transfer, flow resistance, and temperature uniformity enables the tailoring of channel morphology; (2) The Reynolds number dictates adaptive structural evolution, with optimized designs transitioning from wide, low-resistance channels at Re = 60 to densely branched networks at Re = 160. This adaptation enhances heat transfer (increasing from 15458W⋅m−1to35664W⋅m−1) and intensifies flow dissipation (increasing from 2.59×10−5W⋅m−1 to 19.23×10−5W⋅m−1). (3) The machine learning-assisted framework achieves high-fidelity topology optimization predictions, with average errors below 2 % across multi-physics fields. Compared to conventional CFD calculation, the proposed approach accelerates computations by a factor exceeding 7200. Our results show that the proposed method enables real-time, high-fidelity optimization under varied constraints, enhancing computational efficiency and practical applicability for rapid microchannel heat sink design in dynamic engineering environments.
Keywords: Microchannel heat sinks; Topology optimization; Machine learning model