Artificial neural network prediction and multi-objective genetic algorithm optimization of the microchannel heat sink with trapezoidal ribs

Abstract

Trapezoidal rib microchannel heat sinks (TR-MCHS) are widely used for efficient heat dissipation in electronic integrated devices. To enhance the heat transfer and hydraulic performance of TR-MCHS, an optimization method using neural network prediction combined with multi-objective genetic algorithm NSGA-II is proposed. First, the effects of trapezoidal rib geometry parameters and inlet velocity (V) on the Nusselt number (Nu¯) and pressure drop (ΔP) of TR-MCHS are investigated using numerical simulation and Sobol sensitivity analysis. The results show that the trapezoidal rib height (ht) and inlet V have the most significant effect on the TR-MCHS, and the interaction effect between the parameters can be neglected. Then, optimization is performed with Nu¯ and ΔP as objectives. On the obtained Pareto solution set, Nu¯ can be improved by 7.3 % compared to the unoptimized TR-MCHS without consuming additional pumping power. In addition, if the same Nu¯ is obtained as the unoptimized TR-MCHS, the ΔP can be reduced to 223 Pa. On this basis, the TOPSIS method based on entropy weight is developed to solve the optimal compromise solution. The best compromise solution has the Nu¯ of 50.9 and the ΔP of only 184.6 Pa, which is 87.9 Pa lower than that of the unoptimized TR-MCHS. These results have important implications for the design of TR-MCHS for electronics cooling.