Document: NRC Regulatory Guide
Document ID: 82659041-98b0-4721-b25d-c4fb2ea394d0
Document Type: regulatory_guide
Title: An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on Plant-Specific Changes to the Licensing Basis (Rev. 3)
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML1635/ML16358A153.pdf
Revision Date: 2023-06
Chapter: 
Section ID: RG-1.174
CFR Part: 
CFR Title: 

Content:
ing uncertainty on the basic parameter values to generate a probability distribution on the results of the PRA (e.g., CDF, accident sequence frequencies, LERF). However, the analysis should be done to correlate the sample values for different PRA elements from a group to which the same parameter value applies (the so-called state-of-knowledge correlation (SOKC) (G. Apostolakis and S. Kaplan, “Pitfalls in Risk Calculations” Ref. 36). 2.5.3 Model Uncertainty The development of the PRA model is supported by the use of models for specific events or phenomena. In many cases, the industry’s state of knowledge is incomplete, and there may be different opinions on how the models should be formulated. Examples include approaches to modeling human performance, CCFs, and reactor coolant pump seal behavior upon a loss of seal cooling. This gives rise to model uncertainty. In many cases, the appropriateness of the models adopted is not questioned and these models have become, de facto, the consensus models to use. NUREG-1855 defines a consensus model as one that has a publicly available published basis and has been peer reviewed and widely adopted by an appropriate stakeholder group. In addition, widely accepted PRA practices may be regarded as consensus models. Examples of the latter include the use of the constant probability of failure on demand model for standby components and the Poisson model for initiating events. For risk-informed regulatory decisions, the consensus model approach is one that NRC has utilized or accepted for the specific risk-informed application for which it is proposed. For some issues with well-formulated alternative models, PRAs have addressed model uncertainty by using discrete distributions over the alternative models, with the probability associated with a specific model representing the analyst’s degree of belief that the model is the most appropriate. A good example is the characterization of the seismic hazard as different hypotheses