Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident Tolerant Fuel

TL;DR

This paper explores the use of Gaussian Process models within digital twin frameworks to enhance accident tolerant fuel safety analysis, emphasizing uncertainty quantification and sensitivity analysis for nuclear applications.

Contribution

It demonstrates the practical application of Gaussian Process models in digital twins for accident tolerant fuel, highlighting their ability to handle data uncertainties and improve predictive accuracy.

Findings

GP effectively models ATF behavior with noisy data

Uncertainty quantification improves safety assessments

Sensitivity analysis identifies key factors affecting ATF performance

Abstract

The application of digital twin (DT) technology to the nuclear field is one of the challenges in the future development of nuclear energy. Possible applications of DT technology in the nuclear field are expected to be very wide: operate commercial nuclear reactors, monitor spent fuel storage and disposal facilities, and develop new nuclear systems. As U.S. Nuclear Regulatory Committee (NRC) recently announced, machine learning (ML) and artificial intelligence (AI) will be new domains in the nuclear field. Considering the data science perspective, Gaussian Process (GP) has proven to be an ML algorithm for modeling and simulation components of the digital twin framework, specifically for the accident tolerant fuel (ATF) concepts. ATF is one of the high-priority areas for both the U.S. Department of Energy (DOE) and NRC. GP's inherent treatment of lack of data, missing data, and data inconsistencies (noisy/erroneous data) present in the ATF concepts make it an attractive machine learning algorithm for implementation in the DT framework. This chapter focuses on the practical demonstration of GP and its applicability to the DT framework for predicting ATF.