在本文中,设计了两个新颖的微型散热器(MHS),并使用数值方法进行了热分析。使用的流体是具有高体积分数(VOF)的勃姆石氧化铝-水纳米流体(NF)。进行了研究,以确定各种纳米颗粒(NP)形状的影响,如血小板砖,刀片,气缸,和Os。散热器(HS)由铜制成,NFs通过中间进入,并通过HS侧面的四个出口退出。有限元方法用于模拟HS中的NFs流动和传热。为此,使用多物理COMSOL软件。HS温度的最大值和中间值(T-MAX和T-Mid),热电阻(TH-R),传热系数(h),FOM,等。,对不同的NP形状进行了研究,雷诺数(Re)为300、1000和1700,VOFs为0、3和6%。这项工作的重要成果之一是具有矩形翅片的HS具有更好的热效率。此外,发现Re的增加增加了热传递。总的来说,将具有高VOFs的NPs添加到MHS在热量方面是不合适的。Os形状是最好的NP形状,对于高NPVOF,血小板形状是最差的NP形状。当NPs被添加到MHS时,MHS的温度平均下降了2.8或2.19K,根据包含在MHS内的针鳍的形式(圆形或方形)。在具有圆形和方形针鳍的MHS中添加NPs使压降增加了13.5%和13.3%,分别,当Re=1700时。
In this paper, two novel micro heat sinks (MHSs) were designed and subjected to thermal analysis using a numerical method. The fluid used was Boehmite alumina-water nanofluid (NFs) with high volume fractions (VOFs). Studies were conducted to determine the influence of a variety of nanoparticle (NP) shapes, such as platelet brick, blade, cylinder, and Os. The heatsink (HS) was made of copper, and the NFs entered it through the middle and exited via four outlets at the side of the HS. The finite element method was used to simulate the NFs flow and heat transfer in the HSs. For this purpose, Multi Physics COMSOL software was used. The maximum and middle values of HS temperature (T-MAX and T-Mid), thermal resistance (TH-R), heat transfer coefficient (h), FOM, etc., were studied for different NP shapes, and with Reynolds numbers (Re) of 300, 1000, and 1700, and VOFs of 0, 3, and 6%. One of the important outcomes of this work was the better thermal efficiency of the HS with rectangular fins. Moreover, it was discovered that a rise in Re increased the heat transfer. In general, adding NPs with high VOFs to MHSs is not appropriate in terms of heat. The Os shape was the best NP shape, and the platelet shape was the worst NP shape for high NPVOF. When NPs were added to an MHS, the temperature of the MHS dropped by an average of 2.8 or 2.19 K, depending on the form of the pin-fins contained inside the MHS (circular or square). The addition of NPs in the MHS with circular and square pin-fins enhanced the pressure drop by 13.5% and 13.3%, respectively, when the Re = 1700.