Bayesian statistical model for cladding high-temperature burst under loss-of-coolant accident conditions

Abstract

ABSTRACT This study developed a probabilistic determination model with respect to cladding high-temperature burst conditions based on the Bayesian statistical method to reasonably evaluate fuel behaviors under loss-of-coolant accident conditions, including fuel fragmentation, relocation, and dispersal. The candidate models (correlations) were based on the widely accepted empirical model established based on nonirradiated fuel cladding data. Explanatory variables were added to improve the applicability of these models with respect to irradiated materials and generalization performance. The posterior predictive distribution of each candidate model was evaluated using Bayesian estimation comprising 238 sets of high-temperature burst test data. The generalization performance was evaluated using information criteria. The results of model evaluation showed improved predictive performance by considering the effect of hydrogen content. A comparison with burnup as an alternative explanatory variable confirmed that hydrogen content was the better parameter and other burnup-associated effects, such as irradiation hardening of the metal matrix and oxide growth (reduction of the metal matrix), were less dominant under burst conditions.