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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Uncertainties in Predictions of Material Performance Using Experimental Data That Is Only Distantly Related to the System of Interest
verfasst von : Wayne E. King, Athanasios Arsenlis, Charles Tong, William L. Oberkampf
Erschienen in: Uncertainty Quantification in Scientific Computing
Verlag: Springer Berlin Heidelberg
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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.