Document: NUREG-0800
Document ID: e32f0820-4e33-476e-aa36-4ca8c2c64af0
Document Type: srp
Title: Use of Probabilistic Risk Assessment in Plant-Specific, Risk-Informed Decisionmaking:
Source: NUREG-0800
Source URL: https://www.nrc.gov/docs/ML0119/ML011940192.pdf
Revision Date: 2023-06
Chapter: 19
Section ID: 19.0
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le assurance that the proposal fulfills Principle 4 (discussed in Section II). The assessment of whether this has been demonstrated must be made on the basis of an understanding of the contributors to the PRA results, and on the impacts of the uncertainties (both those that are explicitly accounted for in the results and those that are not). This is a somewhat subjective process; therefore, in order to complete the assessment, reviewers must carefully document the reasoning behind the decisions. SRP 19-21 As discussed in RG 1.174, PRA values can be affected by particular modeling assumptions that are a response to the uncertainty regarding how to correctly model the plant response following an initiating event. Thus, it is important that uncertainties in the PRA results be taken into account in assessing the risk impact and in the risk-informed decisionmaking process to demonstrate t:he robustness of the results. The scope of the required uncertainty analysis is a function of the role that the quantification results play in the decision, and on the significance of the calculated change. The general approach to accounting for uncertainty is discussed in Section 2.2.5 of RG 1. 174. In that discussion, uncertainties are categorized as parameter, model, and completeness uncertainties. In assessing analysis of uncertainties, reviewers should consider the types and sources of uncertainties identified by the licensee, and how those uncertainties have been addressed with reference to the decision guidelines. Specifically, review guidance is as follows. Parameter uncertainty: Reviewers should determine whether the licensee has accounted for parameter uncertainties in an appropriate manner so that the estimated values for ACDF, ALERF, CDF, and LERF can be regarded as equivalent to mean values. However, this does not imply that a detailed propagation of uncertainties is always necessary; in many cases, it is possible to show that a point estimate is an acceptable approximation