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Research Papers: Heat and Mass Transfer

Heat Transfer Modeling of Spent Nuclear Fuel Using Uncertainty Quantification and Polynomial Chaos Expansion

[+] Author and Article Information
Imane Khalil, Quinn Pratt, Harrison Schmachtenberger

Shiley-Marcos Department of
Mechanical Engineering,
University of San Diego,
5998 Alcala Park,
San Diego, CA 92110

Roger Ghanem

Sonny Astani Department of Civil
and Environmental Engineering,
3610 S. Vermont Street,
University of Southern California,
Los Angeles, CA 90089

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received March 27, 2017; final manuscript received June 7, 2017; published online September 6, 2017. Editor: Portonovo S. Ayyaswamy.

J. Heat Transfer 140(2), 022001 (Sep 06, 2017) (9 pages) Paper No: HT-17-1171; doi: 10.1115/1.4037501 History: Received March 27, 2017; Revised June 07, 2017

A novel method that incorporates uncertainty quantification (UQ) into numerical simulations of heat transfer for a 9 × 9 square array of spent nuclear fuel (SNF) assemblies in a boiling water reactor (BWR) is presented in this paper. The results predict the maximum mean temperature at the center of the 9 × 9 BWR fuel assembly to be 462 K using a range of fuel burn-up power. Current related modeling techniques used to predict the heat transfer and the maximum temperature inside SNF assemblies rely on commercial codes and address the uncertainty in the input parameters by running separate simulations for different input parameters. The utility of leveraging polynomial chaos expansion (PCE) to develop a surrogate model that permits the efficient evaluation of the distribution of temperature and heat transfer while accounting for all uncertain input parameters to the model is explored and validated for a complex case of heat transfer that could be substituted with other problems of intricacy. UQ computational methods generated results that are encompassing continuous ranges of variable parameters that also served to conduct sensitivity analysis on heat transfer simulations of SNF assemblies with respect to physically relevant parameters. A two-dimensional (2D) model is used to describe the physical processes within the fuel assembly, and a second-order PCE is used to characterize the dependence of center temperature on ten input parameters.

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Figures

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Fig. 1

Fluent computational mesh for the 9 × 9 storage basket

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Fig. 2

Lower right corner of computational model of the 9 × 9 storage basket

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Fig. 3

Schematic of typical dry cask storage

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Fig. 4

Mean temperature (Kelvin) throughout the assembly for high boundary wall temperature

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Fig. 5

Mean temperature (Kelvin) throughout the assembly for low boundary wall temperature

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Fig. 6

PDF for the center temperature in the high boundary wall temperature case

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Fig. 7

PDF for the center temperature in the low boundary wall temperature case

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Fig. 8

Mean velocity streamlines for high boundary wall temperature case

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Fig. 9

Perspective view of the COV at each point in the mesh; predictions about the center of the basket will typically exhibit more variation

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Fig. 10

Spatial dependence of the sensitivity of the temperature with respect to variations in the specific heat of helium

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Fig. 11

Spatial dependence of the sensitivity of the temperature with respect to variations in the thermal conductivity of zircaloy for the high boundary wall temperature case

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Fig. 12

Spatial dependence of the sensitivity of the temperature with respect to variations in the thermal conductivity of zircaloy for the low boundary wall temperature case

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Fig. 13

Spatial dependence of the sensitivity of the temperature with respect to variations in the thermal conductivity of UO2 for the high boundary wall temperature case

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Fig. 14

Spatial dependence of the sensitivity of the temperature with respect to variations in the fuel emissivity for the high boundary wall temperature case

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Fig. 15

Spatial dependence of the sensitivity of the temperature with respect to variations in the specific heat of the UO2 for the high boundary wall temperature case

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Fig. 16

Comparison of the PDFs for the center temperature created from the n = 6, p = 2 and n = 10, p = 1 simulations

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Fig. 17

Comparison between the PDFs for the center temperature, the hotter one being the result of +50% heat generation rate

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