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

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.