Document: NRC Regulatory Guide
Document ID: 0d99822b-1af0-41be-a088-75b961301b4f
Document Type: regulatory_guide
Title: Regulatory Guidance on the Alternate Pressurized Thermal Shock Rule + HISTORY - HISTORY 03/2015 – DG-1299 , Proposed Revision 0
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML1405/ML14056A011.pdf
Revision Date: 2023-06
Chapter: 
Section ID: RG-1.230
CFR Part: 
CFR Title: 

Content:
ns. ii. Factors to Consider When the Step 2 Statistical Test are not Satisfied: When any of the statistical tests are not satisfied, values of ΔT30 predicted using Equation (5) may under-estimate the embrittlement magnitude. Therefore, review of the data for that heat, including all parameters used in Equation (5) and the data used to determine the CVN curve for the material in the unirradiated condition, should be performed. The most appropriate approach may not be a heat-specific adjustment of the ETC predictions in all cases. For example, statistically significant differences may indicate situations where the available data (i.e., the measured ΔT30 values and/or the composition and exposure values associated with the measured ΔT30 values) may not be accurate, thereby making adjustment of the ETC predictions to match these data unnecessary. Assessment of the data should consider, but not be limited to, the following factors: DG-1299, Page 12 • RTNDT(U) value: A records investigation of the RTNDT(U) value, and/or the performance of additional testing of archival material, may provide a more accurate estimate of RTNDT(U), which may explain the reason for not satisfying the mean and/or outlier tests. • Irradiated T30 values: While most CVN energy vs. temperature curves (from which T30 values are estimated) are based on ≈8 to 12 individual measurements, some data sets are more limited, which can lead to increased uncertainty in the values of T30. In the event that any of the statistical tests are not satisfied, a review of the individual CVN energy vs. temperature curves may help reveal the cause. • Composition and exposure variables: The input variables to Equation (5) are subject to variability and are often based on limited data. However, the predictions of Equation (5) are very sensitive to the value of the input variables, particularly Cu content, fluence, temperature, and Ni content. If a sensitivity analysis reveals that small variations of the