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Development of the PIRT for the Small-Break Loss-of-Coolant Accident With High Pressure Injection Failure

[+] Author Affiliations
Ikuo Kinoshita, Toshihide Torige, Michio Murase, Yoshitaka Yoshida

Institute of Nuclear Safety System, Inc. (INSS), Mihama, Fukui, Japan

Minoru Yamada

MHI Nuclear Engineering Co., Ltd., Yokohama, Kanagawa, Japan

Paper No. IMECE2013-63682, pp. V06BT07A047; 10 pages
  • ASME 2013 International Mechanical Engineering Congress and Exposition
  • Volume 6B: Energy
  • San Diego, California, USA, November 15–21, 2013
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5629-1
  • Copyright © 2013 by ASME


In order to improve the reliability of using the accident management procedure “low pressure injection by intentional depressurization of the steam generator secondary side” in a small-break loss-of-coolant accident (SBLOCA) with high pressure injection (HPI) failure, the statistical safety evaluation method is applied to the thermal hydraulic analysis of this event. The cornerstone of this method is the development of the phenomena identification ranking table (PIRT). In this paper, the PIRT was developed for the SBLOCA with HPI failure in a conventional PWR plant. The safety evaluation parameter was the peak cladding temperature (PCT). In the PIRT development, the phenomena identification was conducted by dividing the event progression into five phases: blowdown, natural circulation, loop seal, boil-off, and core recovery. As a result, 24 important phenomena were extracted in the PIRT, and the reasons used for ranking of these phenomena were described extensively. In addition, sensitivity analyses using the RELAP5/MOD3.2 code were performed for the most important phenomena. Uncertainty of the analytical models of the three phenomena: “uncovered core heat transfer”, “CCFL at the inlet of the U-tubes”, and “critical flow of the break” had a large influence on the predicted PCT. Therefore it is important to investigate separate effect tests for these phenomena thoroughly in order to prevent the model uncertainty distributions from being extended excessively.

Copyright © 2013 by ASME



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