Fast approach of Pareto-optimal solution recommendation to multi-objective optimal design of serpentine-channel heat sink

A multi-objective structural design of a serpentine channel heat sink is presented in this paper. In the structural modelling of the heat sink, channel width, fin width, channel height and inlet velocity are defined as the design variables, ‘total thermal resistance’ and the ‘pressure drop’ as the t...

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Published inApplied thermal engineering Vol. 70; no. 1; pp. 263 - 273
Main Authors Chen, Yi, Peng, Bei, Hao, Xiaohong, Xie, Gongnan
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 05.09.2014
Elsevier
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ISSN1359-4311
DOI10.1016/j.applthermaleng.2014.05.004

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Summary:A multi-objective structural design of a serpentine channel heat sink is presented in this paper. In the structural modelling of the heat sink, channel width, fin width, channel height and inlet velocity are defined as the design variables, ‘total thermal resistance’ and the ‘pressure drop’ as the two objectives, subject to constraints of fixed length and width of the heat sink. In this study, a multi-objective artificial swarm fish algorithm with a variable population size using a non-dominated sorting method (MOAFNS) has been developed to handle the optimisation, in which fast approach of Pareto-optimal solution recommendation using the Pareto risk index is proposed to handle the optimal trade-offs between the two conflicting thermal objectives. Then, the optimal solutions have been validated by performing related experiments. The Pareto-front indicates a trade-off between ‘total thermal resistance’ and ‘pressure drop’. Numerical results and experimental data have reached an agreement that reduction in both thermal resistance and pressure drop can be achieved via determination of channel configuration and inlet velocity using MOAFNS, which results in desired thermal performance of the heat sink. [Display omitted] •The structural modelling of serpentine channel heat sink and experimental validation.•Multi-objective artificial fish swarm algorithm using non-dominated sorting method.•The approach of fast Pareto-optimal solution recommendation (FPR).•Pareto risk index (PRI).•Trend indices of mean average precision (mAP) and mean standard deviation (mSTD).
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ISSN:1359-4311
DOI:10.1016/j.applthermaleng.2014.05.004