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Research Papers: Electronic Cooling

# Genetic Algorithm Based Optimization of PCM Based Heat Sinks and Effect of Heat Sink Parameters on Operational Time

[+] Author and Article Information
Atul Nagose, Ankit Somani, Aviral Shrot

Heat Transfer and Thermal Power Laboratory, Department of Mechanical Engineering,  Indian Institute of Technology Madras, Chennai 600 036, India

Arunn Narasimhan1

Heat Transfer and Thermal Power Laboratory, Department of Mechanical Engineering,  Indian Institute of Technology Madras, Chennai 600 036, Indiaarunn@iitm.ac.in

1

Corresponding author.

J. Heat Transfer 130(1), 011401 (Jan 28, 2008) (8 pages) doi:10.1115/1.2780182 History: Received January 12, 2007; Revised May 07, 2007; Published January 28, 2008

## Abstract

Using an approach that couples genetic algorithm (GA) with conventional numerical simulations, optimization of the geometric configuration of a phase-change material based heat sink (PBHS) is performed in this paper. The optimization is done to maximize the sink operational time (SOT), which is the time for the top surface temperature of the PBHS to reach the critical electronics temperature (CET). An optimal solution for this complex multiparameter problem is sought using GA, with the standard numerical simulation seeking the SOT forming a crucial step in the algorithm. For constant heat dissipation from the electronics (constant heat flux) and for three typical PBHS depths $(A)$, predictive empirical relations are deduced from the GA based simulation results. These correlations relate the SOT to the amount of phase change material to be used in the PBHS $(φ)$, the PBHS depth $(A)$, and the heat-spreader thickness $(s)$, a hitherto unconsidered variable in such designs, to the best of the authors’ knowledge. The results show that for all of the typical PBHS depths considered, the optimal heat-spreader thickness is 2.5% of the PBHS depth. The developed correlations predict the simulated results within 4.6% for SOT and 0.32% for $ϕ$ and empowers one to design a PBHS configuration with maximum SOT for a given space restriction or the most compact PBHS design for a given SOT.

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## Figures

Figure 4

Isotherms for three arbitrary E-PBHS designs (see Table 3 for specific values) at the end of SOT

Figure 5

SOT and melt time versus Φ for w=2mm and w=4mm

Figure 1

(a) Schematic representation of PBHS; (b) domain chosen for analysis (E-PBHS)

Figure 2

E-PBHS with boundary conditions

Figure 3

Comparison of melt front solver with benchmark solution

Figure 6

Flowchart of the GA solution procedure

Figure 7

Binary representation of the “individual” E-PBHS considered in the GA solution procedure

Figure 8

Fitness function, Eq. 7, evolution with generations for A=(a)0.02m; (b) 0.06m

Figure 9

Values of s for optimal designs determined by the GA procedure

Figure 10

SOT values for different A for optimal designs determined by the GA procedure

Figure 11

Φ values for different A for optimal designs determined by the GA procedure

## Errata

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