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U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (DOE)initiated a future-focused research project to assess the regulatory viabilityof machine learning (ML) and artificial intelligence (AI)-driven Digital Twins(DTs) for nuclear applications. Advanced accident tolerant fuel (ATF) is one ofthe priority focus areas of the DOE/ NRC. DTs have the potential to transformthe nuclear energy sector in the coming years by incorporating risk-informeddecision-making into the Accelerated Fuel Qualification (AFQ) process for ATF.A DT framework can offer game-changing yet practical and informed solutions tothe complex problem of qualifying advanced ATFs. However, novel ATF technologysuffers from a couple of challenges, such as (i) Data unavailability; (ii) Lackof data, missing data; and (iii) Model uncertainty. These challenges must beresolved to gain the trust in DT framework development. In addition,DT-enabling technologies consist of three major areas: (i) modeling andsimulation (M&S), covering uncertainty quantification (UQ), sensitivityanalysis (SA), data analytics through ML/AI, physics-based models, anddata-informed modeling, (ii) Advanced sensors/instrumentation, and (iii) Datamanagement. UQ and SA are important segments of DT-enabling technologies toensure trustworthiness, which need to be implemented to meet the DTrequirement. Considering the regulatory standpoint of the modeling andsimulation (M&S) aspect of DT, UQ and SA are paramount to the success of DTframework in terms of multi-criteria and risk-informed decision-making. In thisstudy, the adaptability of polynomial chaos expansion (PCE) based UQ/SA in anon-intrusive method in BISON was investigated to ensure M&S aspects of the AFQfor ATF. This study introduces the ML-based UQ and SA methods while exhibitingactual applications to the finite element-based nuclear fuel performance code.

Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology