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Research Papers

Multi-Objective Design Optimization of Multiple Microchannel Heat Transfer Systems Based on Multiple Prioritized Preferences

[+] Author and Article Information
Po Ting Lin

Mem. ASME
Department of Mechanical Engineering,
Chung Yuan Christian University,
200 Chungpei Road,
Chungli, Taoyuan 32023, Taiwan;
Department of Mechanical Engineering,
National Taiwan University of
Science and Technology,
43 Keelung Road, Sec. 4,
Taipei 10607, Taiwan
e-mail: potinglin223@gmail.com

Mark Christian E. Manuel

School of Mechanical and
Manufacturing Engineering,
Mapua Institute of Technology,
Muralla Street,
Intramuros, Manila 1002, Philippines
e-mail: marchm.090407@gmail.com

Jingru Zhang

Corning, Inc.,
23 Indian Pipe Court,
Painted Post, NY 14870
e-mail: ruharvard@gmail.com

Yogesh Jaluria

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
Rutgers,
The State University of New Jersey,
98 Brett Road,
Piscataway, NJ 08854
e-mail: jaluria@jove.rutgers.edu

Hae Chang Gea

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
Rutgers,
The State University of New Jersey,
98 Brett Road,
Piscataway, NJ 08854
e-mail: gea@rci.rutgers.edu

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received March 8, 2016; final manuscript received December 13, 2016; published online March 7, 2017. Assoc. Editor: Samuel Sami.

J. Thermal Sci. Eng. Appl 9(2), 021011 (Mar 07, 2017) (9 pages) Paper No: TSEA-16-1056; doi: 10.1115/1.4035836 History: Received March 08, 2016; Revised December 13, 2016

Accelerated development in the field of electronics and integrated circuit technology further pushed the need for better heat dissipating devices with reduced component dimensions. In the design optimization of microchannel heat transfer systems, multiple objectives must be satisfied but correlations limit the satisfaction levels. End users define their preferences associated with the desired quality/quantity of each parameter and specify the priorities among each preference. In this paper, an optimization strategy based on the prioritized performances is developed to find the optimal design variables for the preferences in three different aspects namely: minimized thermal resistances, minimized pressure drop, and maximized heat flux. The preferences are often fuzzy and correlated but can be modeled mathematically using Gaussian membership functions with respect to different levels of user preferences. The overall performances are maximized to find the most favorable solution on the Pareto frontier. Two different types of single-phase liquid cooling (straight and U-shaped microchannel heat sinks) have been utilized as heat exchangers of electronic chips and made as practical examples for the proposed optimization strategy. The optimal design points vary with respect to the priorities of the preferences. The proposed methodology finds the most favored solution on the Pareto frontiers. It is novel to reveal that the chosen significant factors were maximized with results yielding to lower thermal resistance, lower pressure drop, and higher heat flux in the microchannel heat sink based on the design preferences with different priorities.

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Figures

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Fig. 1

Schematics and computational domains of straight and U-shaped microchannel heat sink models: (a) schematic of straight design, (b) computational domain of straight design, (c) schematic of U-shaped design, and (d) computational domain of U-shaped design [3,42]

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Fig. 2

Isosurfaces of heat flux for: (a) straight and (b) U-shaped microchannel models

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Fig. 3

Pareto frontier of straight microchannel designs in: (a) angular and (b) orthographic views

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Fig. 4

Pareto frontier of U-shaped microchannel designs in: (a) angular and (b) orthographic views

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Fig. 5

Illustrations of the most preferred designs on: (a) two-dimensional and (b) three-dimensional Pareto frontiers

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Fig. 6

Gaussian membership function for the preferences of: (a) thermal resistance, (b) pressure drop, and (c) heat flux

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Fig. 7

Gaussian membership function of correlated preferences of flow rate, pressure drop, and heat flux

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