OPTIMIZATION OF MICRO-CHANNEL HEAT SINK BASED ON GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK

Abstract

In order to efficiently solve the problem of optimization of the micro-channel heat sink, an optimization strategy combining intelligent algorithms and CFD was proposed. The micro-channel heat sink with the trapezoidal cavity and sol-id/slotted oval pins was proposed to enhance heat transfer. The aspect ratio, dis-tance from the center of the oval pin to the center of the cavity, and slot thickness were design variables. The thermal resistance and pumping power of the micro-channel heat sink were objective functions. Within the selected range of design variables, thirty groups of uniformly sampled sample points were obtained by the Latin hypercube experiment. The 3-D model was established by SOLIDWORKS software, and the numerical simulation was carried out by using FLUENT soft-ware. The genetic algorithm optimized back propagation neural network to con-struct the prediction model, and the simulated data of Latin hypercube sampling were trained to obtain the non-linear mapping relationship between design vari-ables and objective functions. The optimal combination of structural parameters of the micro-channel heat sink was obtained by optimization of the genetic algo-rithm, which was verified by numerical simulation. The results show that the op-timization scheme was suitable for getting the optimal value of the structural pa-rameters of the micro-channel heat sink, which provided a reference for the op-timal design of the micro-channel heat sink.