Document: NRC Regulatory Guide
Document ID: aa450282-f5b0-4dd2-b41a-917a7491deb2
Document Type: regulatory_guide
Title: Preparing Probabilistic Fracture Mechanics (Rev. 0)
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML2133/ML21334A158.pdf
Revision Date: 2023-05
Chapter: 
Section ID: RG-1.245
CFR Part: 
CFR Title: 

Content:
of-knowledge uncertainty.” The American Society of Mechanical Engineers/American Nuclear Society PRA standard (Ref. 24) defines epistemic uncertainty as “the uncertainty attributable to incomplete knowledge about a phenomenon that affects our ability to model it. Epistemic uncertainty is reflected in ranges of values for parameters, a range of viable models, the level of model detail, multiple expert interpretations, and statistical confidence. In principle, epistemic uncertainty can be reduced by the accumulation of additional information. (Epistemic uncertainty is sometimes also called ‘modeling uncertainty.’)” expert judgment Information (or opinion) provided by one or more technical experts based on their experience and knowledge. Used when there is a lack of information, for example, if certain parameter values are unknown, or there are questions about phenomenology in accident progression. May be part of a structured approach, such as expert elicitation, but is not necessarily as formal. May be the opinion of one or more experts, whereas expert elicitation is a highly structured process in which the opinions of several experts are sought, collected, and aggregated in a very formal way. RG 1.245 Revision 0, Page 28 global sensitivity analysis The study of how the uncertainty in the output or quantity of interest of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input. The term “global” ensures that the analysis considers more than just local or one-factor-at-a-time effects. Hence, interactions and nonlinearities are important components of a global statistical sensitivity analysis. important input variable An input variable whose uncertainty contributes substantially to the uncertainty in the response. input Data or parameters that users can specify for a model; the output of the model varies as a function of the inputs, which can consist of physical values (e.g., material properties,