2021 journal article

Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release

ANNALS OF NUCLEAR ENERGY, 153.

co-author countries: United States of America 🇺🇸
author keywords: Doped fuel; Variational Bayesian Monte Carlo (VBMC); Bayesian inference; Kriging; Principal Component Analysis (PCA)
Source: Web Of Science
Added: February 15, 2021

One of the advanced nuclear fuel concepts for current commercial water-cooled reactors focuses on microstructural modification of UO2 fuel via dopants. Dopants can effectively promote grain growth and suppress fission gas release (FGR), a key parameter that dictates the overall nuclear fuel performance. This work improves the BISON FGR model for chromia/alumina-doped UO2 fuel through statistical calibration with in-reactor experimental data. The high computing cost and nonintrusive nature of BISON limit the application of conventional techniques under the Bayesian framework. Dimensionality reduction is performed using principal component analysis (PCA) to deal with the FGR time series data. Kriging is used as metamodel of BISON to reduce the computing cost. A novel optimization framework, Variational Bayesian Monte Carlo (VBMC) is demonstrated as a low-cost nonintrusive approach for Bayesian calibration. The performance of VBMC is compared to the conventional statistical Markov Chain Monte Carlo (MCMC) sampling showing similar accuracy but superior efficiency.