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:
ociated 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 lead to different hazard curves, which can be used to develop a discrete probability distribution of the initiating event frequency for earthquakes. Other examples can be found in the Level 2 analysis. Another approach to addressing model uncertainty has been to adjust the results of a single model through the use of an adjustment factor. However it is formulated, an explicit representation of model uncertainty can be propagated through the analysis in the same way as parameter uncertainty. More typically, however, particularly in the Level 1 analysis, the use of different models would result in the need for a different structure (e.g., with different thermal hydraulic models used to determine success criteria). In such cases, uncertainties in the choice of an appropriate model are typically addressed by making assumptions and, as in the case of the component failure models discussed above, adopting a specific model. DG-1285, Page 33 In interpreting the results of a PRA, it is important to develop an understanding of the impact of a specific assumption or choice of model on the predictions of the PRA. This is true even when the model uncertainty is treated probabilistically, since the probabilities, or weights, given to different models would be subjective. The impact of using alternative assumptions or models may be addressed by performing appropriate sensitivity studies or by using qualitative arguments, based on an understanding of the contributors to the results and how they are impacted by the change in assumptions or models. The impact of making specific modeling approximations may be explored in a similar manner. 2.5.4 Completeness Uncertainty Completeness is not in itself an uncertainty, but a reflection of scope limitations. The result is, however, an