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Research Papers: Heat Exchangers

Multi-objective Design Optimization of Branching, Multifloor, Counterflow Microheat Exchangers

[+] Author and Article Information
Abas Abdoli

Department of Mechanical
and Materials Engineering,
Florida International University,
MAIDROC Laboratory, EC2960,
10555 West Flagler Street, EC3462,
Miami, FL 33174
e-mail: aabdo004@fiu.edu

George S. Dulikravich

Professor and Director of MAIDROC
Fellow ASME
Department of Mechanical
and Materials Engineering,
Florida International University,
MAIDROC Laboratory, EC2960,
10555 West Flagler Street, EC3462,
Miami, FL 33174
e-mail: dulikrav@fiu.edu

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received September 14, 2013; final manuscript received June 20, 2014; published online July 15, 2014. Assoc. Editor: Oronzio Manca.

J. Heat Transfer 136(10), 101801 (Jul 15, 2014) (10 pages) Paper No: HT-13-1483; doi: 10.1115/1.4027911 History: Received September 14, 2013; Revised June 20, 2014

Heat removal capacity, coolant pumping power requirement, and surface temperature nonuniformity are three major challenges facing single-phase flow microchannel compact heat exchangers. In this paper multi-objective optimization has been performed to increase heat removal capacity, and decrease pumping power and temperature nonuniformity in complex networks of microchannels. Three-dimensional (3D) four-floor configurations of counterflow branching networks of microchannels were optimized to increase heat removal capacity from surrounding silicon substrate (15 × 15 × 2 mm). Each floor has four different branching subnetworks with opposite flow direction with respect to the next one. Each branching subnetwork has four inlets and one outlet. Branching patterns of each of these subnetworks could be different from the others. Quasi-3D conjugate heat transfer analysis has been performed by developing a software package which uses quasi-1D thermofluid analysis and a 3D steady heat conduction analysis. These two solvers were coupled through their common boundaries representing surfaces of the cooling microchannels. Using quasi-3D conjugate analysis was found to require one order of magnitude less computing time than a fully 3D conjugate heat transfer analysis while offering comparable accuracy for these types of application. The analysis package is capable of generating 3D branching networks with random topologies. Multi-objective optimization using modeFRONTIER software was performed using response surface approximation and genetic algorithm. Diameters and branching pattern of each subnetwork and coolant flow direction on each floor were design variables of multi-objective optimization. Maximizing heat removal capacity, while minimizing coolant pumping power requirement and temperature nonuniformity on the hot surface, were three simultaneous objectives of the optimization. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with four-floor microchannel cooling networks. A fully 3D thermofluid analysis was performed for one of the optimal designs to confirm the accuracy of results obtained by the quasi-3D simulation package used in this paper.

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Figures

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Fig. 1

Microchannel configuration: (a) 3D four-floor microchannels and (b) four branching subnetworks on one floor

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Fig. 2

Temperature distribution for a nonoptimized configuration: (a) hot surface (having large temperature variations CV = 1.711 × 10−2) and (b) entire 3D substrate

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Fig. 3

Temperature distribution for nonoptimized microchannel walls: (a) four-floor microchannels, (b) first floor, (c) second floor, (d) third floor, and (e) fourth floor

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Fig. 8

Temperature distribution for Pareto optimized design No. 21 when using fully 3D conjugate analysis: (a) hot surface and (b) 3D substrate

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Fig. 9

Convergence histories for temperature field inside the substrate obtained using two conjugate analysis codes

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Fig. 7

Temperature distribution on (a) four-floor microchannels, (b) first floor, (c) second floor, (d) third floor, and (e) fourth floor of the Pareto optimized cooling network No. 21

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Fig. 6

Temperature distribution for Pareto optimized cooling network No. 21 when using quasi-3D conjugate analysis: (a) hot surface and (b) 3D substrate

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Fig. 5

CV versus total heat removed for initial population, virtual Pareto designs, and real Pareto designs

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Fig. 4

Thermal energy removed versus pumping power requirement for initial population, virtual Pareto, and real Pareto-optimal designs

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