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 solid/slotted oval pins was proposed to enhance heat transfer. The aspect ratio, distance 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 construct the prediction model, and the simulated data of Latin hypercube sampling were trained to obtain the non-linear mapping relationship between design variables and objective functions. The optimal combination of structural parameters of the micro-channel heat sink was obtained by optimization of the genetic algorithm, which was verified by numerical simulation. The results show that the optimization scheme was suitable for getting the optimal value of the structural parameters of the micro-channel heat sink, which provided a reference for the optimal design of the micro-channel heat sink.