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Research Papers

Analysis of Computational Fluid Dynamics Code FLUENT Capabilities for Supercritical Water Heat-Transfer Applications in Vertical Bare Tubes

[+] Author and Article Information
Amjad Farah

Mem. ASME Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology,
2000 Simcoe Street North, Oshawa, ON L1J 5S1, Canada
e-mail: amjad.farah@uoit.ca

Glenn Harvel

Mem. ASME Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology,
2000 Simcoe Street North, Oshawa, ON L1J 5S1, Canada
e-mail: glenn.harvel@uoit.ca

Igor Pioro

Mem. ASME Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology,
2000 Simcoe Street North, Oshawa, ON L1J 5S1, Canada

1Corresponding author.

Manuscript received August 5, 2015; final manuscript received January 14, 2016; published online June 17, 2016. Assoc. Editor: Leon Cizelj.

ASME J of Nuclear Rad Sci 2(3), 031016 (Jun 17, 2016) (12 pages) Paper No: NERS-15-1171; doi: 10.1115/1.4032642 History: Received August 05, 2015; Accepted January 21, 2016

In this paper, the computational fluid dynamics (CFD) code FLUENT was used to predict wall-temperature profiles inside vertical bare tubes with supercritical water (SCW) as the cooling medium, to assess the capabilities of FLUENT for SCW heat-transfer applications. Numerical results are compared to experimental data and current one-dimensional (1D) models represented by existing heat-transfer empirical correlations. Wall-temperature and heat-transfer coefficients were analyzed to select the best model to describe the fluid flow before, at, and after the pseudocritical region. kϵ and kω turbulent models were evaluated in the process, with variations in the submodel parameters such as viscous heating, thermal effects, and low-Reynolds-number correction. Results of the analysis show a fit of ±10% for wall temperatures using the SST kω model within the deteriorated heat-transfer regime and less than ±5% within the normal heat-transfer regime. The accuracy of the model is higher than any empirical correlation tested in the mentioned regimes and provides additional information about the multidimensional effects between the bulk-fluid and wall temperatures.

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Figures

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Fig. 1

Thermophysical properties of water within pseudocritical-point region

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Fig. 2

(a) Graphical representation of computational domain. (b) Graphical representation of the 2D mesh in the 3D space.

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Fig. 3

Initial experimental, calculated, and simulated results for low-range mass and heat fluxes in the 4-m mesh

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Fig. 4

Initial experimental, calculated, and simulated results for mid-range mass and heat fluxes in the 2- and 4-m meshes

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Fig. 5

Control case for sensitivity analysis

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Fig. 6

Pressure variation effect on bulk-fluid and wall-temperature distributions

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Fig. 7

Specific heat trends with pressure variation

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Fig. 8

Heat-flux variation effect on bulk-fluid temperature distributions

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Fig. 9

Heat-flux variation effect on wall-temperature distributions

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Fig. 10

Experimental, calculated, and CFD simulated results for NHT in 2-m computational domains

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Fig. 11

Turbulent kinetic energy based on flow centerline for the RKE and SST models, 1–3 m

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Fig. 12

Experimental, calculated, and simulated results for DHT in 2-m computational domains

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Fig. 13

Turbulent kinetic energy based on flow centerline for the RKE and SST models, 1–3 m

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Fig. 14

Uncertainty in wall temperatures for Mokry et al. correlation [12]

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Fig. 15

Uncertainty in HTCs for Mokry et al. correlation [12]

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Fig. 16

Uncertainty in wall temperatures for RKE model

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Fig. 17

Uncertainty in wall temperatures for SST model

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Fig. 18

Uncertainty in HTC values for RKE model

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Fig. 19

Uncertainty in HTC values for SST model

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