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:
[Acceptance criteria met with at least one order of magnitude margin] AND [no importance sampling AND no surrogate models used] • No sampling uncertainty characterization recommended as long as the uncertainty is sufficiently small relative to the margin. b SC-2A [Acceptance criteria met with at least one order of magnitude margin] AND [use of importance sampling OR surrogate models OR both] • Describe the approach used for assessing statistical convergence, with one method needed for sampling uncertainty characterization. • Explain the approach used for characterizing sampling uncertainty. • Justify why the sampling uncertainty is small enough for the intended purpose (i.e., why statistical convergence is sufficient for the intended purpose). • Describe how sampling uncertainty is used in the interpretation of the results. SC-2B [Acceptance criteria met with at least one order of magnitude margin] AND [use of importance sampling OR surrogate models OR both] AND [separation of aleatory and epistemic uncertainties is implemented in the PFM code] • See the submittal guidelines for SC-2A and distinguish between epistemic and aleatory means and standard deviations. SC-3A [Acceptance criteria met with less than one order of magnitude margin] • See the submittal guidelines for SC-2A and provide two different methods for sampling uncertainty characterization. SC-3B [Acceptance criteria met with less than one order of magnitude margin] AND [separation of aleatory and epistemic uncertainties is implemented in the PFM code] • See the submittal guidelines for SC-3A and give a sample size convergence analysis for both the aleatory and epistemic sample sizes. a Data type may have an impact on the convergence category. Continuous outputs can be category SC-1, but binary outputs inherently must be category SC-2 or SC-3 unless epistemic and aleatory uncertainties are separated. b Some assessment of uncertainty is necessary, even if qualitative, as long as the