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


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.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 2

Physical model of a pin-fin heat sink

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Figure 3

Surface spline control points

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Figure 4

Fin heights distribution

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Figure 5

Mesh generation of a solid domain

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Figure 6

Mesh generation of a fluid domain

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Figure 7

Pareto fronts and reference front

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Figure 8

Some heat sinks from the best front of PBIL

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Figure 9

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

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Figure 10

Zoom-in of Fig. 9

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Figure 11

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

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Figure 12

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

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Figure 13

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

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Figure 14

Distribution of H1 –H16 of the Pareto optimum solutions




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