Session: CS-15-03 Mechanical Properties of Nuclear Graphite and Their Implementation in Codes and Standards (Joint with MF-29) – 3

Paper Number: 107369

107369 - Finite Element Model Mesh Refinement Effects on Qualification of Nuclear Grade Graphite Core Components 

The American Society of Mechanical Engineers (ASME) provides two design-by-analysis probabilistic methodologies for determining acceptance of nuclear grade graphite core components. The assessments can be characterized by three parts: (1) a finite element (FE) model of the component, (2) a Weibull probability density function that characterizes the experimental tensile strength distribution and (3) the post-processor, which combines the FE model and the Weibull strength distribution in accordance with the full and simplified assessments to determine component acceptance. It is known that the level of mesh refinement in FE models can affect the modeled component’s stress distribution. Depending on the component geometry, the stress distribution may converge with sufficient refinement. The question was opened as to whether the acceptance decision resulting from the full and simplified assessments might change even with sufficient mesh refinement. This paper explores that question using experimental strength results for a dog-bone geometry for two graphite grades, NBG-18 and PCEA. In the application of the simplified assessment, mesh refinement did cause minor variation in the peak equivalent stress which can alter the outcome of the assessment. When using the larger minimum link volumes in the 2019 Code version, the percent change in full assessment failure probabilities was less than 2% for the sufficiently refined meshes. When applying the 2021 Code version, the percent changes in POF were 20-30%, suggesting smaller minimum link volumes may require more extensive mesh refinements to achieve POF convergence.

Presenting Author: Andrea Mack Idaho National Laboratory

Presenting Author Biography: ANDREA L. MACK received the B.S. degree in mathematics in 2014 and the M.S. degree in statistics in 2017 from Montana State University (MSU).

She is currently a Statistician at Idaho National Laboratory. Andrea provides statistical support to various projects and is currently supporting the Advanced Gas Reactor, Nuclear Regulatory Commission and the Digital Radiography and Computed Tomography programs. Her research interests include failure probability analysis, signal processing, error propagation, graph analysis, Bayesian population variability modeling and spatial statistics.

Prior to INL, Andrea was the Assistant Director at the Statistical Consulting and Research Services at MSU and completed a graduate mathematical sciences internship at the National Renewable Energy Laboratory.

Authors:

Andrea Mack Idaho National Laboratory
William Hoffman Idaho National Laboratory
Joseph Bass Idaho National Laboratory
William Windes Idaho National Laboratory