Machine-learning-based multi-objective optimization of a diamond manifold microchannel heat sink

The thermal management of ultra-high heat fluxes exceeding 2000 W/cm² has become an increasingly critical challenge for advanced electronic systems. To address this challenge, this study proposes a novel diamond manifold microchannel (D-MMC) heat sink and employs a machine-learning-based multi-objective optimization approach to systematically optimize its structure. The optimization is conducted in two sequential stages: first, the geometry of the microchannel layer is optimized; second, the manifold layer is co-optimized based on the already-optimized microchannel configuration. In each stage, an artificial neural network (ANN) is trained as a surrogate model to map structural parameters to thermal-hydraulic performance and is then integrated with a genetic algorithm (GA) for the Pareto-optimal search. The final optimized D-MMC heat sink incorporates microchannels with serrated walls and a manifold structure modulated by width parameters. Notably, by synergistically integrating the optimized manifold microchannel architecture with the superior thermal conductivity of diamond (2200 W/m·K), the proposed D-MMC heat sink achieves effective cooling under an ultra-high heat flux of 3500 W/cm² while maintaining a remarkably low pressure drop of only 8.1 kPa. This work demonstrates a potent design paradigm for next-generation ultra-high-heat-flux thermal management.