Success criteria analysis plays a key role in the development of realistic probabilistic safety/risk assessment (PSA/PRA) model because it provides supporting information regarding the response of complex nuclear power plant systems to different accident conditions. The current paper performs plant specific success criteria analysis for steam generator tube rupture (SGTR) accident in a typical pressurized water reactor (PWR) and demonstrates implementation of the obtained best estimate results on a risk model which has been initially developed based on expert judgment and general plant design data. The modifications on the risk model include configuration of the safety systems as well as the event tree structure. The updated PSA model shows 50% reduction in the plant core damage frequency (CDF) in comparison to the base case risk model. This highlights the importance of success criteria analysis for the development of a realistic PSA model in risk informed applications.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
ANS/ASME. (2014). Requirements for low power and shutdown probabilistic risk assessment (ANS/ASME 58.22).Search in Google Scholar
ASME/ANS. (2009). Standard for level 1/large early release frequency PRA for NPP applications (ASME/ANS RA-Sa-2009).Search in Google Scholar
ASME/ANS. (2021). Probabilistic risk assessment standard for advanced non-LWR nuclear power plants (ASME/ANS RA-S-1.4–2021).Search in Google Scholar
Campbell, S. (2020). NUREG-2236, confirmatory thermal-hydraulic analysis to support specific success criteria in the standardized plant analysis risk models-Duane Arnold. U.S. Nuclear Regulatory Commission, Washington, DC.Search in Google Scholar
Esmaili, H. (2011). Confirmatory thermal-hydraulic analysis to support NUREG-1953, specific success criteria in the standardized plant analysis risk models—surry and peach bottom. U.S. Nuclear Regulatory Commission, Washington, DC.Search in Google Scholar
Farahani, A.Z., Yousefpour, F., and Hoseyni, S.M. (2017). Sensitivity analysis for thermo-hydraulics model of a Westinghouse type PWR: verification of the simulation results. Kerntechnik 82: 289–302, https://doi.org/10.3139/124.110627.Search in Google Scholar
Hoseyni, S.M., Karimi, K., and Mohammadnia, M. (2017). Success criteria analysis in support of probabilistic safety assessment for nuclear power plants; application on SGTR accident. Nucl. Sci. Tech. 28: 1–17, https://doi.org/10.1007/s41365-017-0193-z.Search in Google Scholar
IAEA (2006). IAEA TECDOC-1511, determining the quality of probabilistic safety assessment (PSA) for applications in nuclear power plants. International Atomic Energy Agency, Vienna.Search in Google Scholar
National Diet of Japan Fukushima Nuclear Accident Independent Investigation Commission. (2011). The official report of the Fukushima nuclear accident. Independent Investigation Commission, Tokyo.Search in Google Scholar
OECD-NEA. (2017). Safety research opportunities post-fukushima, initial report of the senior expert group (NEA/CSNI/R(2016)19).Search in Google Scholar
Rasmussen, N.C. (1975). Reactor safety study. An assessment of accident risks in U. S. commercial nuclear power plants. Executive Summary (WASH-1400 (NUREG-75/014)).Search in Google Scholar
Rogovin, M. (1980). NUREG/CR—1250, three mile island: a report to the commissioners and to the public. U.S. Nuclear Regulatory Commission, Washington, DC.Search in Google Scholar
USNRC (1983). NUREG/CR-2300, PRA procedures guide: a guide to the performance of probabilistic risk assessments for nuclear power plants. U.S. Nuclear Regulatory Commission, New York.Search in Google Scholar
USNRC (1990). NUREG-1150, severe accident risks: an assessment for five U.S. nuclear power plants, s.l. U.S. Nuclear Regulatory Commission, Washington, DC.Search in Google Scholar
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