Purpose

This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work.

Design/methodology/approach

A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate.

Findings

In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model.

Originality/value

Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

中文翻译：

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GaN HEMT器件热管理的数值模拟和微通道参数优化

目的

本研究旨在满足氮化镓 (GaN) 高电子迁移率晶体管 (HEMT) 器件的热管理，在这项工作中设计和优化了微通道冷却。

设计/方法/方法

结合计算流体动力学（CFD）和多目标进化算法（MOEA）对微通道参数进行数值模拟分析，分析微通道的热流特性。设计变量包括微通道的宽度和数量，优化目标是在恒定体积流量下最小化总热阻和压降。

发现

在优化过程中，压降的降低有助于增加热阻，从而导致高结温，反之亦然。并且帕累托最优前沿是优化目标之间的权衡曲线，通过MOEA方法获得。最后，在Pareto-optimal front上进行K-means聚类算法，提出三个代表点来验证模型的准确性。

原创性/价值

研究了每个设计变量对两个目标和温度分布的影响。提供了最小热阻和压降之间的关系，可以为 GaN HEMT 器件冷却中的微通道设计提供一些基本方向。