Heat transfer enhancement in multijet micropin fin heat sink

散热片 材料科学 传热 压力降 机械 强化传热 传热系数 热的 强化传热 计算机模拟 机械工程 下降(电信) 喷射(流体) 涡流 工作液 雷诺数 计算流体力学 对流换热 热力学 有限体积法 数值分析 体积热力学 热阻 电子设备冷却
作者
Mohammad Owais Qidwai,Irfan Anjum Badruddin,Sarfaraz Kamangar,Noor Zaman Khan,Mohammad Anas Khan,Mohammad Nawaz Khan
出处
期刊:Numerical Heat Transfer Part A-applications [Taylor & Francis]
卷期号:86 (9): 2806-2825 被引量:5
标识
DOI:10.1080/10407782.2023.2294349
摘要

The thermal performance of tiny heat sinks with liquid cooling is being studied in order to solve the issue of electronics cooling. Increasing thermal performance while lowering pressure drop and maintaining a constant substrate temperature is a difficult task. The purpose of this study is to harvest the useful features of combination of microjet and micropin fins and explore the influence of geometrical features on fluid structures and heat transfer. To comprehend and assess the performance of a multijet heat sink with a micropin fin, three-dimensional numerical simulation is used in this inquiry. The three parameters under consideration are, the ratio of the jet diameter to the pin fin diameter (α), which ranges from 1.5 to 6.0; the ratio of the jet diameter to the jet standoff distance (β), which ranges from 0.75 to 1.5; and the pin fin pitch is determined by the ratio of the pin fin diameter to the pitch length (λ), which ranges from 0.1 to 0.8. The grid independence test is done to ensure accuracy in numerical results. The numerical scheme is validated by comparing numerical results with that of experimental results from literature. The results obtained from numerical simulation are analyzed. It has been found that improving heat transfer is not just caused by expanding the contact surface area; fluid structure vortex generation also affects overall thermal performance. Better overall performance is produced at lower values of α, β and higher values of λ. The ideal design parameters are α = 1.99, β = 1.431, and λ = 0.699, according to the optimization of geometrical parameters using a radial basis neural network surrogate model and particle swarm optimization. The jet Reynolds number is found to have no significant impact on the overall performance.
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