Thermo-hydraulic performance optimization of a disk-shaped microchannel heat sink applying computational fluid dynamics, artificial neural network, and response surface methodology

The current research focuses on optimizing the Nusselt number (Nu) and pressure drop (ΔP) in a bionic fractal heat sink. The artificial neural network (ANN) and response surface methodology (RSM) were used to model the thermos-hydraulic behavior of the MCHS. The aspect ratios of t/b (cavities' upper side to bottom side ratio) and h/b (cavities’ height to bottom side ratio), as well as the Reynolds number, were set as the independent variables in both ANN and RSM models. After finding the optimum state for the copper-made MCHS (containing the optimum design of the cavities along with the best applied velocity), different materials were tested and compared with the base case (heat sink made of copper). The obtained results indicated that both ANN and RSM models (with determination coefficient of 99.9 %) could exactly anticipate heat transfer and ΔP to a large extent. To achieve the optimal design of the microchannel heat sink (MCHS) with the objective of maximizing Nu and minimizing ΔP, the efficiency index of the device was evaluated. The analysis revealed that the highest efficiency index (1.070 by RSM and 1.067 by ANN methods) was attained when the aspect ratios were t/b = 0.2, h/b = 0.2, and the Reynolds number was 1000. Next, the effect of the different materials on heat sink performance was investigated, and it was observed that by reducing the thermal conductivity, the thermal resistance of the heat sink increased and its overall performance decreased.

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