Multi-objective optimizations of structural parameter determination for serpentine channel heat sink

Xuekang Li, Xiaohong Hao, Yi Chen, Muhao Zhang, Bei Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an approach for modeling and optimization of the channel geometry of a serpentine channel heat sink using multi-objective genetic algorithm. A simple thermal resistance network model was developed to investigate the overall thermal performance of the serpentine channel heat sink. Based on a number of simulations, bend loss coefficient correlation for 1000<Re<2200 was obtained which was function of the aspect ratio (a), ratio of fins width to channel width (b). In this study, two objectives minimization of overall thermal resistance and pressure drop are carried out using multi-objective genetic algorithms. The channel width, fin width, channel height and inlet velocity are variables to be optimized subject to constraints of fixed length and width of heat sink. The study indicates that reduction in both thermal resistance and pressure drop can be achieved by optimizing the channel configuration and the inlet velocity.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computing
Subtitle of host publication16th European Conference, EvoApplications 2013
EditorsAnna I. Esparcia-Alcazar
PublisherSpringer
Pages449-458
Number of pages10
ISBN (Electronic)9783642371929
ISBN (Print)9783642371912
DOIs
Publication statusPublished - 25 Feb 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7835

Keywords

  • heat sink
  • serpentine channel
  • bend loss coefficient
  • multi-objective optimizations

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    Li, X., Hao, X., Chen, Y., Zhang, M., & Peng, B. (2013). Multi-objective optimizations of structural parameter determination for serpentine channel heat sink. In A. I. Esparcia-Alcazar (Ed.), Applications of Evolutionary Computing: 16th European Conference, EvoApplications 2013 (pp. 449-458). (Lecture Notes in Computer Science; Vol. 7835). Springer. https://doi.org/10.1007/978-3-642-37192-9_45