Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

TL;DR

This paper explores the use of machine learning for uncertainty quantification and sensitivity analysis in Digital Twins applied to nuclear fuel performance, aiming to support regulatory decision-making.

Contribution

It introduces ML-based uncertainty and sensitivity analysis methods and demonstrates their application to the BISON nuclear fuel performance code.

Findings

ML methods effectively quantify uncertainty in BISON simulations

Sensitivity analysis identifies key parameters affecting fuel performance

Enhances regulatory confidence in digital twin applications

Abstract

To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the U.S. Department of Energy (DOE). A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, uncertainty quantification and sensitivity analysis are paramount to the DT framework's success in terms of multi-criteria and risk-informed decision-making. This chapter introduces the ML-based uncertainty quantification and sensitivity analysis methods while exhibiting actual applications to the finite element-based nuclear fuel performance code BISON.