Abstract

Multi-objective optimization of nanofluids in a microchannel heat sink was conducted to determine the highest Nusselt number and the lowest pressure drop. This was done by creating a design study consisting of four nanoparticles of Al₂O₃, CuO, ZnO and SiO₂ with four diameters of 10, 30, 60 and 100 nm, nanoparticle volumetric concentrations of 0.001, 0.005, 0.01 and 0.05 and four base fluids of water (W), ethylene glycol (EG), W/EG (50/50) and W/EG (60/40). Every possible combination of these four input parameters created 256 design points for the numerical simulation. The outcome of the numerical simulation depended on the performance of heat transfer and pressure drop. A modified non-dominated sorting genetic algorithm was then used to determine the highest Nusselt number and the lowest pressure drop based on the numerical results of the 256 design points. The outcome of the optimization was that silicon dioxide–water (SiO₂) nanofluid gave the optimal combination of heat transfer and pressure drop. The results also showed that the nanoparticle diameter had a small effect on the pressure drop in the microchannel heat sink and an increase in the nanoparticle volumetric concentration increased the heat transfer coefficient.

摘要

对微通道散热器中的纳米流体进行多目标优化，以确定最高的努塞尔数和最低的压降。这是通过创建由 Al ₂ O ₃、CuO、ZnO 和 SiO ₂四种纳米颗粒组成的设计研究来完成的四种直径为 10、30、60 和 100 nm，纳米颗粒体积浓度为 0.001、0.005、0.01 和 0.05，四种基础流体为水 (W)、乙二醇 (EG)、W/EG (50/50) 和 W /EG (60/40)。这四个输入参数的每种可能组合为数值模拟创建了 256 个设计点。数值模拟的结果取决于传热性能和压降。然后使用改进的非支配排序遗传算法根据 256 个设计点的数值结果确定最高努塞尔数和最低压降。优化的结果是二氧化硅-水（SiO ₂) 纳米流体给出了传热和压降的最佳组合。结果还表明，纳米颗粒直径对微通道散热器中的压降影响很小，纳米颗粒体积浓度的增加增加了传热系数。