Micro-pin fin heat sinks are widely used to cool miniature devices. The flow characteristics and cooling performance of these heat sinks are highly dependent on their geometric configuration. Previous studies have focused on optimizing the design so that the pressure drop decreases, while the heat transfer performance is maintained. However, limited numbers of geometries have been explored, mainly considering only homogeneous pin fin arrays. In this study, we propose a neural network-based regression approach called the flow-learned building block (FLBB) and develop an effective parametric study and optimization for micro-pin fin heat sinks including heterogeneous geometries. The prediction capabilities of the FLBB are verified by comparing the predicted results with direct numerical simulation results for various pitch distances, pin sizes, and arrangements at Reynolds numbers from 1 to 100. Furthermore, we demonstrate the applicability of the FLBB to different working fluids, quantified by the Prandtl number (0.71 Pr 5.86). Leveraging the reliable and effective prediction capabilities of our neural network-based approach, we perform parametric studies of micro-pin fin heat sinks for working fluids of air and water with the aim of minimizing the pump power and achieving uniform heat transfer along the pin fins.
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