Vortex generators in heat sinks: Design, optimisation, applications and future trends

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY Results in Engineering Pub Date : 2025-03-01 Epub Date: 2025-02-01 DOI:10.1016/j.rineng.2025.104216
Mohammad Ismail , Abdullah Masoud Ali , Sol-Carolina Costa Pereira
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Abstract

Vortex generators (VGs) have been identified as an exceedingly effective method in augmenting heat transfer in minichannels, a significant feature of cutting-edge heat sinks, heat exchangers and electronics cooling tools. This paper aims to discuss the various types of vortex generators, specifically delta winglet, rectangular winglet, trapezoidal and perforated, as well as their impact on improving convective heat transfer. This study focuses on the configurations that define geometric parameters, including the angle of attack, height and spacing in relation to the improvement in thermal or hydraulic performance. Strategies such as nanofluids, dimples, Response Surface Methodology analysis and Artificial Neural Networks are crucial to improve VG designs to maximise thermal efficiency and minimise pressure loss. Additionally, the paper considers potential trends, such as the further miniaturisation of the VGs in terms of micro-level utilisation, more intricate VG shapes, together with the development of smart VGs that can alter their configuration depending on the current thermal conditions. To conclude, this review will provide beneficial information pertaining to current VG technology, the application of VG in high-performance cooling systems, and also identify areas for future research concerning VG technology.
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散热器中的涡流发生器:设计、优化、应用和未来趋势
涡发生器(VGs)已被确定为一种非常有效的方法,在增加热传递的微型通道,一个重要的特点,尖端的散热器,热交换器和电子冷却工具。本文旨在讨论各种类型的涡发生器,特别是三角小翼、矩形小翼、梯形和穿孔小翼,以及它们对改善对流换热的影响。这项研究的重点是确定几何参数的配置,包括攻角、高度和间距,这些参数与热性能或水力性能的改善有关。纳米流体、酒窝、响应面分析和人工神经网络等策略对于改进VG设计以最大化热效率和最小化压力损失至关重要。此外,本文还考虑了潜在的趋势,例如在微观层面利用方面VG的进一步小型化,更复杂的VG形状,以及可以根据当前热条件改变其配置的智能VG的发展。总之,这篇综述将提供有关当前VG技术的有益信息,VG在高性能冷却系统中的应用,并确定未来关于VG技术的研究领域。
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来源期刊
Results in Engineering
Results in Engineering Engineering-Engineering (all)
CiteScore
5.80
自引率
34.00%
发文量
441
审稿时长
47 days
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