期刊
APPLIED THERMAL ENGINEERING
卷 54, 期 1, 页码 65-77出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2012.10.056
关键词
Phase change materials (PCMs); Electronic equipment; n-Eicosane; Artificial neural networks; Genetic algorithm
This paper reports the results of an experimental investigation carried out to characterize the thermal performance of different configurations of phase change material (PCM) based pin fin heat sinks. Paraffin wax and n-eicosane are used as the PCMs. Aluminium is used to make the heat sinks and the volume fraction is varied by changing the number of pin fins. Baseline comparisons are done with a heat sink filled with PCM, without any fin. The effect of PCM volume fraction on the heat transfer performance is also studied. The results showed that there exists sufficient scope to optimize the thermal design of the heat sink. An Artificial Neural Network - Genetic Algorithm hybrid algorithm is then developed to determine the optimum configuration of the pin fin heat sink that maximizes the operating time for the n-eicosane based heat sink. The resulting optima was found to be valid even for the paraffin wax based PCM. (C) 2013 Elsevier Ltd. All rights reserved.
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