Uncertainties in Predictions of Material Performance Using Experimental Data That Is Only Distantly Related to the System of Interest

Abstract

There is a need for predictive material "aging" models in the nuclear energy industry, where applications include life extension of existing reactors, the development of high burnup fuels, and dry cask storage of used nuclear fuel. These problems require extrapolating from the validation domain, where there is available experimental data, to the application domain, where there is little or no experimental data. The need for predictive material aging models will drive the need for associated assessments of the uncertainties in the predictions. Methods to quantify uncertainties in model predictions, using experimental data that is only distantly related to the application domain, are discussed in this paper.

General H2O Outgassing Model Applicable to Silica-Filled Silicones

“…A two-stage approach was implemented to obtain the optimized kinetic parameters from the modified Page model. First, model parameters are estimated using the nonintrusive uncertainty quantification code package called PSUADE. − Here, the Latin Hypercube (LH) sampling method is employed to generate a large number of sample points (>50,000 points) covering a large parametric space. Each sample point consists of a set of the modified Page model parameters (i.e., E a , k o , and n ).…”

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

“…Most of the current VV&UQ approaches rely on the correctness of the physical equations in the simulation model. Thus, it is helpful to preserve the physical principles, but to add extrapolative power to the correction [128], instead of only extrapolating the error structure [106].…”