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Research Papers: Thermal Systems

Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

[+] Author and Article Information
Ya Ge

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
1037 Luoyu Road,
Wuhan 430074, China
e-mail: geya@hust.edu.cn

Feng Shan

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
1037 Luoyu Road,
Wuhan 430074, China
e-mail: shanfeng@hust.edu.cn

Zhichun Liu

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
1037 Luoyu Road,
Wuhan 430074, China
e-mail: zcliu@hust.edu.cn

Wei Liu

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
1037 Luoyu Road,
Wuhan 430074, China
e-mail: w_liu@hust.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received September 24, 2016; final manuscript received June 29, 2017; published online September 26, 2017. Assoc. Editor: Amitabh Narain.

J. Heat Transfer 140(2), 022803 (Sep 26, 2017) (7 pages) Paper No: HT-16-1601; doi: 10.1115/1.4037643 History: Received September 24, 2016; Revised June 29, 2017

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

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Figures

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Fig. 1

Schematic diagrams of MCHS

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Fig. 2

Computational domain

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Fig. 3

Flowchart of the optimization work

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Fig. 4

Comparison of present thermal resistance, θ, with previous work obtained by Xie et al. [3], for Ww = Wc = 0.5 mm, Hb = 1 mm, and uin = 1 m s−1

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Fig. 5

Comparison of present pressure drop, Δp, with previous work obtained by Xie et al. [3], for Ww = Wc = 0.5 mm, Hb = 1 mm, and uin = 1 m s−1

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Fig. 6

Distribution of the Pareto front

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Fig. 7

Variation of four optimal design parameters along with the Pareto front

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Fig. 8

Variation of inlet velocity, inlet area, and channel numbers along with the Pareto front

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Fig. 9

Variation of heat sink width, hydraulic diameter, and Reynolds number along with the Pareto front

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Fig. 10

Performances of the initial and different optimal individuals

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