The United States Nuclear Regulatory Commission (USNRC) relies on Probabilistic Risk Assessment (PRA) as one of the main pillars of its risk-informed regulatory and oversight functions. In 2011, the South Texas Project Nuclear Operating Company (STPNOC) initiated a risk-informed project to resolve Generic Safety Issue 191 (GSI-191), which is related to the performance of the emergency core cooling system (ECCS) following a loss of coolant accident (LOCA) [1, 2, 3]. The progress of the research and its implementation have also significantly benefitted from the discussions and feedback received from regulatory representatives. An integrative framework was developed to explicitly provide failure probabilities for the post-LOCA PRA basic events associated with the concerns raised in GSI-191 [4]. These basic event probabilities are estimated using the CASA Grande program, which was developed as part of the project. This program encompasses the time-dependent modeling of the underlying physical phenomena of the basic events and the propagation of the uncertainties in the physical models. This paper summarizes the elements of the integrative framework including PRA and the CASA Grande program, as well as the input parameters, assumptions, methodology, and results of the STPNOC analysis. The results show that the risk of core damage or large early release related to the concerns raised in GSI-191 in the as-built, as-operated design for STPNOC is very small (as defined in Regulatory Guide 1.174).

Fig. 1 Integrated Probabilistic Risk Assessment (I-PRA ) for GSI-191


  1. Mohaghegh, Z., Kee, E., Reihani, S., Kazemi, R., et al.  “Risk-Informed Resolution of Generic Safety Issue 191”, ANS PSA 2013 International Topical Meeting on Probabilistic Safety Assessment and Analysis, 2013.
  2. Sande, T.D., Zigler, G.L., Kee, E.J., Letellier, B.C., Grantom, C.R., Mohaghegh, Z., 2012. The Benefits of Using a Risk-Informed Approach to Resolving GSI-191, 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, pp. 725-734.
  3. Kee, J. Hasenbein, A. Zolan, P. Grissom, S. Reihani, Z. Mohaghegh, F. Yilmaz, B. Letellier, V. Moiseytseva, R. Vaghetto, and T. Sakurahara, “RoverD: Use of Test Data in GSI-191 Risk Assessment,” Nuclear Technology, vol. 196, pp. 270-291, 2016.
  4. Bui, T. Sakurahara, J. Pence, S. Reihani, E. Kee, Z. Mohaghegh, An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants, Reliability Engineering & System Safety, Vol. 185, pp. 405-428, (2019)
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