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Multi-objective optimization of water-cooled pinfin heatsinks

机译:水冷针鳍式散热器的多目标优化

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This paper presents the multi-objective optimization of water-cooled pinfin heatsinks. The heat transfer rate and pressure drop were the objective functions, and four parameters (height, diameter, longitudinal pitch, and transverse pitch) of pinfin geometry were the design variables. The relationship between the objective functions and the design variables for pinfin heatsink was calculated by using our own semi-analytical equations. We applied two kinds of constraints. One is the clearance distance between the tip of the pins and the flow channel wall, and the other is the minimum gap between pins considering cost-effectiveness and manufacturability. The best trade-off curves between the pressure drop and the heat transfer rate were calculated using genetic algorithm. We found the similarity of the best tradeoff curves under two different constraint conditions when the multiplication of the pin height and the minimum gap between pins were the same. We also showed that the small clearance causes the reduction of pressure drop while maintaining high heat transfer performance. Our calculated solutions were validated by comparing with experimental results. The pressure drops could be predicted within an error of 30%, and the effective heat transfer rates agreed within an error of 10%.
机译:本文提出了水冷pinfin散热器的多目标优化。传热速率和压降是目标函数,pinfin几何形状的四个参数(高度,直径,纵向节距和横向节距)是设计变量。 Pinfin散热器的目标函数和设计变量之间的关系通过使用我们自己的半解析方程式进行了计算。我们应用了两种约束。一个是销钉尖端与流道壁之间的间隙距离,另一个是考虑到成本效益和可制造性的销钉之间的最小间隙。利用遗传算法计算了压降与传热率之间的最佳折衷曲线。当引脚高度的乘积和引脚之间的最小间隙相同时,我们发现在两个不同的约束条件下最佳折衷曲线的相似性。我们还表明,小间隙可在保持高传热性能的同时降低压降。通过与实验结果进行比较,验证了我们计算出的解决方案。可以预测压降在30%的误差范围内,有效传热率在10%的误差范围内。

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