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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Content:
nt-level importance measures can provide valuable input to the integrated decisionmaking process. b. Review Guidance and Procedures Risk ranking results from a PRA can be affected by many factors, the most important being the model assumptions anid techniques (e.g., for modeling of human reliability or common cause failures), the data used, or the success criteria chosen. Reviewers should therefore evaluate the licensee's PRA as part of the overall review process. Appendix A to this SRP chapter presents guidance for this review. In addition to using a PRA of appropriate quality for the application, the licensee should demonstrate the robustness of risk ranking results for conditions and parameters that might not be addressed in the base PRA. Therefore, when importance measures are used to group components or human actions as low safety-significant contributors, the information to be provided to the integrated decisionmaking process should include sensitivity studies and/or other evaluations to demonstrate the sensitivity of the ranking results to the important PRA modeling techniques, assumptions, and data. In assessing this information, reviewers should consider the follow ng issues: Different risk metrics: Reviewers should ensure that the licensee's ranking process adequately considered risk in terms of both CDF and LERF. Completeness of risk model: Reviewers should ensure that, when determining safety significance contributions using an internal events PRA, the licensee also considered external events, as well as shutdown and low-power initiators, either by PRA modeling or by the integrated decisionrnaking process (as detailed in Section C.2 and Appendix B to this SRP chapter). SRP 19-C2 Sensitivity analysis for component data uncertainties: The licensee should have addressed the sensitivity of component categorizations to uncertainties in the parameter values. Reviewers should be satisfied that SSC categorization is not affected by data uncertainties.