Thoughts on Improving Confidence in Probabilistic Fracture Mechanics Analyses

Abstract

In the past, the US Nuclear Regulatory Commission (NRC) has typically regulated the use of nuclear structural materials on a deterministic basis. Safety factors, margins, and conservatisms were used to account for model and input uncertainty. However, in the mid-1990s, the NRC issued a policy statement that encouraged the use of Probabilistic Risk Assessments (PRA) to improve safety decision making and improve regulatory efficiency. Since that time, the NRC has made progress in its efforts to implement risk-informed and performance-based approaches into its regulation and continues to revisit and update the approaches on a regular basis. A major component to the overall safety of nuclear structures is the fracture behavior of the materials. Consensus codes and standards responsible for the design and analysis of such structures, such as ASME Boiler and Pressure Vessel code, typically rely on conservative fracture models with applied safety factors and conservative bounding inputs to account for the numerous uncertainties that may be present. Improving the reliability of such models by truly understanding the impacts of the assumptions and uncertainties becomes difficult because of the conservative nature of the models and inputs and the inadequate documentation of the basis for safety factors. As a subset of PRA, probabilistic fracture mechanics (PFM) is an analysis technique that allows greater insight into the structural integrity of components than similar deterministic analyses. PFM allows the direct representation of uncertainties through the use of best-estimate models and distributed inputs. This analysis methodology permits determination of the direct impact of uncertainties on the results, which gives the user the ability to determine and possibly refine the specific drivers to the problem. However, PFM analyses can be more complicated and difficult to conduct than deterministic analyses. Determining validated best-estimate models, developing input distributions with limited data, characterizing and propagating input and model uncertainty, and understanding the impacts of problem assumptions on the adequacy of the results, can complicate the development and approval of PFM analyses in a regulatory application. This paper provides some thoughts on how to improve confidence in structural analyses performed using PFM, by focusing on topics such as solution convergence, input distribution determination, uncertainty analyses, sensitivity analyses (to determine impact of uncertainties on result) and sensitivity studies (to determine impact of mean values on the results). By determining the main drivers to the probabilistic results and investigating the impacts of the assumption made to develop those drivers, the confidence in the overall results can be greatly improved.