Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release. (April 2021)

• Record Type: Journal Article
• Authors: Che, Yifeng; Wu, Xu; Pastore, Giovanni; Li, Wei; Shirvan, Koroush
• Is Part Of: Annals of nuclear energy. Volume 153(2021)
• Publication Date: 2021-04
• Subjects: Doped fuel -- Variational Bayesian Monte Carlo (VBMC) -- Bayesian inference -- Kriging -- Principal Component Analysis (PCA); Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.108046
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Fission gas model in BISON was calibrated for chromia/alumina-doped UO2 fuel. Principal component analysis utilized to deal with high-dimensional time series data. Kriging metamodel constructed to facilitate inference with high fidelity code. Variational Bayesian Monte Carlo is demonstrated as a sample-efficient method. Variational Bayesian Monte Carlo compared to that of Markov Chain Monte Carlo. Abstract: 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.