Canadian Nuclear Standard CSA N285.8, “Technical requirements for in-service evaluation of zirconium alloy pressure tubes in CANDU® reactors”(1), permits the use of probabilistic methods when assessments of the reactor core are performed. A non-mandatory annex has been proposed for inclusion in the CSA Standard N285.8 to provide guidelines for performing uncertainty analysis in probabilistic fitness-for-service evaluations within the scope of this Standard, such as the probabilistic evaluation of leak-before-break. The proposed annex outlines the general approach to uncertainty analysis as being comprised of the following major activities: identification of influential variables, characterization of uncertainties in influential variables, and subsequent propagation of these uncertainties through the evaluation framework or code. The proposed methodology distinguishes between two types of non-deterministic variables by the method used to obtain their best estimate. Uncertainties are classified by their source, and different uncertainty components are considered when the best estimates for the variables of interest are obtained using calibrated parametric models or analyses and when these estimates are obtained using statistical models or analyses.
The application of the proposed guidelines for uncertainty analysis was exercised by performing a pilot study for one of the evaluations within the scope of the CSA Standard N285.8, the probabilistic evaluation of leak-before-break based on a postulated through-wall crack. The pilot study was performed for a representative CANDU reactor unit using the recently developed software code P-LBB that complies with the requirements of Canadian Nuclear Standard CSA N286.7 for quality assurance of analytical, scientific, and design computer programs for nuclear power plants. This paper discusses the approaches used and the results obtained in the second stage of this pilot study, the uncertainty characterization of influential variables identified as discussed in the companion paper presented at the PVP 2018 Conference (PVP2018-85010).
In the proposed methodology, statistical assessment and expert judgment are recognized as two complementary approaches to uncertainty characterization. In this pilot study, the uncertainty characterization was limited to cases where statistical assessment could be used as the primary approach. Parametric uncertainty and uncertainty due to numerical solutions were considered as the uncertainty components for variables represented by parametric models. Residual uncertainty and uncertainty due to imbalances in the model-basis data set were considered as the uncertainty components for variables represented by statistical models. In general, the uncertainty due to numerical solutions was found to be substantially smaller than the parametric uncertainty for variables represented by parametric models, and the uncertainty due to imbalances in the model basis data set was found to be substantially smaller than the residual uncertainty for variables represented by statistical models.
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