Enhancement of heat dissipation rate of a heat sink with perforated fins using CFD and Swarm Intelligence

被引:0
|
作者
Maji, Ambarish [1 ]
Deshamukhya, Tuhin [2 ]
Choubey, Gautam [3 ]
机构
[1] Seacom Engn Coll, Dept Mech Engn, Howrah, W Bengal, India
[2] Barak Valley Engn Coll, Dept Mech Engn, Nirala, Assam, India
[3] Natl Inst Technol NIT Silchar, Dept Mech Engn, Silchar 788010, Assam, India
关键词
particle swarm optimization; perforated fin; system performance; tapered perforations; Teaching Learning Based Algorithm; LEARNING-BASED OPTIMIZATION; DESIGN OPTIMIZATION; PIN FINS; NANOFLUID; FLOW;
D O I
10.1002/htj.23182
中图分类号
O414.1 [热力学];
学科分类号
摘要
This study aims to enhance heat dissipation in a heat sink by using perforated pin fins. The research specifically explores circular pin fins with circular perforations that taper from one end to the other. Through three-dimensional computational fluid dynamics (CFD) simulations, the study evaluates the impact of tapered perforations on heat dissipation, performance, and pressure loss in circular fins. The results indicate that fins with perforations consistently achieve higher heat dissipation rates compared to solid fins, up to certain perforation sizes and numbers. Notably, fins with tapering in perforations, where the inner and outer diameters are 4 and 5 mm, respectively, exhibit the highest system performance, with a maximum increase of 6.1% over fins without tapering. Additionally, two advanced swarm intelligence algorithms, Particle Swarm Optimization (PSO) and Teaching Learning Based Algorithm (TLBO), were employed to assess the performance of heat sinks with both converging and diverging tapered perforations. The optimized results from TLBO were compared with those from PSO for validation. An intriguing discovery of this research is that fins with converging perforations achieve the highest system performance, followed by fins without tapering, and then fins with diverging perforations.
引用
收藏
页码:307 / 329
页数:23
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