0
Research Papers

Multiobjective Optimization of a Pin-Fin Heat Sink Using Evolutionary Algorithms

[+] Author and Article Information
Siwadol Kanyakam

Department of Mechanical Engineering, Faculty of Engineering,  Khon Kaen University, Khon Kaen, 40002 Thailand

Sujin Bureerat1

Department of Mechanical Engineering, Faculty of Engineering,  Khon Kaen University, Khon Kaen, 40002 Thailandsujbur@kku.ac.th

1

Corresponding author.

J. Electron. Packag 134(2), 021008 (Jun 11, 2012) (8 pages) doi:10.1115/1.4006514 History: Received October 07, 2011; Revised March 14, 2012; Published June 11, 2012; Online June 11, 2012

This paper presents the use of multiobjective evolutionary algorithms for the optimal geometrical design of a pin-fin heat sink. The multiobjective design problem is posed to minimize two conflicting objectives: the junction temperature and the fan pumping power of the heat sink. The design variables are mixed integer/continuous. The encoding/decoding process for this mixed integer/continuous design variables is detailed. The multiobjective optimizers employed to solve the design problem are population-based incremental learning, strength Pareto evolutionary algorithm, particles swarm optimization, and archived multiobjective simulated annealing. The approximate Pareto fronts obtained from using the various optimizers are compared based upon the hypervolume and generational distance indicators. From the results, population-based incremental learning (PBIL) outperforms the others. The new design approach is said to be superior to a classical design approach. It is also illustrated that the proposed multiobjective design process leads to better design compared to the current commercial pin-fin heat sinks.

FIGURES IN THIS ARTICLE
<>
Copyright © 2012 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 2

Physical model of a pin-fin heat sink

Grahic Jump Location
Figure 3

Surface spline control points

Grahic Jump Location
Figure 4

Fin heights distribution

Grahic Jump Location
Figure 5

Mesh generation of a solid domain

Grahic Jump Location
Figure 6

Mesh generation of a fluid domain

Grahic Jump Location
Figure 7

Pareto fronts and reference front

Grahic Jump Location
Figure 8

Some heat sinks from the best front of PBIL

Grahic Jump Location
Figure 9

Pareto fronts with and without fin height variation versus commercial heat sinks

Grahic Jump Location
Figure 10

Zoom-in of Fig. 9

Grahic Jump Location
Figure 11

Optimal Pareto fronts of pin-fin and plate-fin heat sinks

Grahic Jump Location
Figure 12

Distribution of wfin , Nf , tb , Vf , and whs versus junction temperature

Grahic Jump Location
Figure 13

Distribution of wfin , Nf , tb , Vf , and whs versus fan pumping power

Grahic Jump Location
Figure 14

Distribution of H1 –H16 of the Pareto optimum solutions

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In