Technical Brief

Conjugate Heat Transfer Predictions for Subcooled Boiling Flow in a Horizontal Channel Using a Volume-of-Fluid Framework

[+] Author and Article Information
M. Langari, Z. Yang, J. F. Dunne, S. Jafari, J.-P. Pirault, C. A. Long

School of Engineering and Informatics,
Department of Engineering and Design,
University of Sussex,
Falmer BN1 9QT, Brighton, UK

J. Thalackottore Jose

Department of Mechanical Engineering,
The Built Environment College of
Engineering and Technology,
University of Derby,
Markeaton Street,
Derby DE22 3AW, UK
e-mail: j.f.dunne@sussex.ac.uk

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received May 31, 2017; final manuscript received February 16, 2018; published online June 7, 2018. Assoc. Editor: Debjyoti Banerjee.

J. Heat Transfer 140(10), 104501 (Jun 07, 2018) (6 pages) Paper No: HT-17-1318; doi: 10.1115/1.4040358 History: Received May 31, 2017; Revised February 16, 2018

The accuracy of computational fluid dynamic (CFD)-based heat transfer predictions have been examined of relevance to liquid cooling of IC engines at high engine loads where some nucleate boiling occurs. Predictions based on (i) the Reynolds Averaged Navier-Stokes (RANS) solution and (ii) large eddy simulation (LES) have been generated. The purpose of these simulations is to establish the role of turbulence modeling on the accuracy and efficiency of heat transfer predictions for engine-like thermal conditions where published experimental data are available. A multiphase mixture modeling approach, with a volume-of-fluid interface-capturing method, has been employed. To predict heat transfer in the boiling regime, the empirical boiling correlation of Rohsenow is used for both RANS and LES. The rate of vapor-mass generation at the wall surface is determined from the heat flux associated with the evaporation phase change. Predictions via CFD are compared with published experimental data showing that LES gives only slightly more accurate temperature predictions compared to RANS but at substantially higher computational cost.

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Grahic Jump Location
Fig. 1

Simulated channel geometry and heating block, dimensions in mm

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Fig. 2

A section of the fluid/solid domain grid

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Fig. 3

Predicted wall temperatures against experimental data [25]

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Fig. 4

Predicted vapor volume fraction at the lowest heat flux of 83 kW/m2

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Fig. 5

A snapshot of wall surface temperature by LES at heat flux of 721 kW/m2

Grahic Jump Location
Fig. 6

Predicted vapor volume fraction at heat flux of 721 kW/m2

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Fig. 7

Predicted vapor volume fractions by different turbulence models at heat flux of 1300 kW/m2

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Fig. 8

Predicted turbulent intensity by realizable k–e turbulence model at heat flux of 721 kW/m2




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