Research Papers: Techniques and Procedures

Application of Fractional Scaling Analysis to Loss of Coolant Accidents: Component Level Scaling for Peak Clad Temperature

[+] Author and Article Information
Ivan Catton

 UCLA–MAE, P.O. Box 951597, 48-121 Engineering IV, Los Angeles, CA 90195-1597catton@ucla.edu

Wolfgang Wulff

11 Hamilton Road, Setauket, NY 11733wolfgangwulff@optonline.net

Novak Zuber

703 New Mark Esplanade, Rockville, MD 20850wulff@bnl.gov

Upendra Rohatgi

 Brookhaven National Laboratory, Building 475B, Upton, NY 11973 USArohatgi@bnl.gov

J. Fluids Eng 131(12), 121401 (Nov 19, 2009) (8 pages) doi:10.1115/1.4000370 History: Received May 28, 2009; Revised September 08, 2009; Published November 19, 2009; Online November 19, 2009

Fractional scaling analysis (FSA) is demonstrated here at the component level for depressurization of nuclear reactor primary systems undergoing a large-break loss of coolant accident. This paper is the third of a three-part sequence. The first paper by Zuber (2005, “Application of Fractional Scaling Analysis (FSA) to Loss of Coolant Accidents (LOCA), Part 1. Methodology Development,” Nucl. Eng. Des., 237, pp. 1593–1607) introduces the FSA method; the second by Wulff (2005, “Application of Fractional Scaling Methodology (FSM) to Loss of Coolant Accidents (LOCA), Part 2. System Level Scaling for System Depressurization,” ASME J. Fluid Eng., to be published) demonstrates FSA at the system level. This paper demonstrates that a single experiment or trustworthy computer simulation, when properly scaled, suffices for large break loss of coolant accident (LBOCAs) in the primary system of a pressurized water reactor and of all related test facilities. FSA, when applied at the system, component, and process levels, serves to synthesize the world-wide wealth of results from analyses and experiments into compact form for efficient storage, transfer, and retrieval of information. This is demonstrated at the component level. It is shown that during LBOCAs, the fuel rod stored energy is the dominant agent of change and that FSA can rank processes quantitatively and thereby objectively in the order of their importance. FSA readily identifies scale distortions. FSA is shown to supercede use of the subjectively implemented phenomena identification and ranking table and to minimize the number of experiments, analyses and computational effort by reducing the evaluation of peak clad temperature (PCT) to a single parameter problem, thus, greatly simplifying uncertainty analysis.

Copyright © 2009 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 4

Scaled LOFT clad temperatures at the location of the peak heating rate-fractional change metric

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Figure 5

A compilation of blowdown peak clad temperature measurements and their relationship to linear heat generation rate

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Figure 1

Dimensionless temperature and its relationship to Biot number and the decay heat fractional change metric

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Figure 2

Normalized PCT for 0.015<NBi<0.03

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Figure 3

LOFT measured clad temperature at the location of the peak heating rate



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