Affiliation:
1. UCLA–MAE, P.O. Box 951597, 48-121 Engineering IV, Los Angeles, CA 90195-1597
2. 11 Hamilton Road, Setauket, NY 11733
3. 703 New Mark Esplanade, Rockville, MD 20850
4. Brookhaven National Laboratory, Building 475B, Upton, NY 11973 USA
Abstract
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 et al. (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 et al. (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.
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5 articles.
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