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The evolution and release of fission gas impacts the performance of UO2nuclear fuel. We have created a Bayesian framework to calibrate a novel modelfor fission gas transport that predicts diffusion rates of uranium and xenon inUO2 under both thermal equilibrium and irradiation conditions. Data sets aretaken from historical diffusion, gas release, and thermodynamic experiments.These data sets consist invariably of summary statistics, including ameasurement value with an associated uncertainty. Our calibration strategy usessynthetic data sets in order to estimate the parameters in the model, such thatthe resulting model predictions agree with the reported summary statistics. Indoing so, the reported uncertainties are effectively reflected in the inferreduncertain parameters. Furthermore, to keep our approach computationallytractable, we replace the fission gas evolution model by a polynomial surrogatemodel with a reduced number of parameters, which are identified using globalsensitivity analysis. We discuss the efficacy of our calibration strategy, andinvestigate how the contribution of the different data sets, taken frommultiple sources in the literature, can be weighted in the likelihood functionconstructed as part of our Bayesian calibration setup, in order to account forthe different number of data points in each set of data summaries. Our resultsindicate a good match between the calibrated diffusivity and non-stoichiometrypredictions and the given data summaries. We demonstrate a good agreementbetween the calibrated xenon diffusivity and the established fit from Turnbullet al. (1982), indicating that the dominant uranium vacancy diffusion mechanismin the model is able to capture the trends in the data.

Bayesian calibration with summary statistics for the prediction of xenon diffusion in UO2 nuclear fuel