AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology

Abstract

In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation codes are necessary. The increased complexity of models demands significant computational resources to quantify their uncertainties. To address this challenge, a data-driven non-intrusive uncertainty quantification method via polynomial chaos expansion is introduced as an efficient strategy within the finite element analysis-based fuel performance code BISON. Models of UO2 and U3Si2 fuels, alongside SiC/SiC cladding material, were prepared to demonstrate the proposed method. The impact of four independent uncertain input variables on the system output was quantified, requiring fewer than 100 BISON simulations for each model. This approach not only accelerates the modeling and simulation task but also enhances the reliability in the development of DT-enabling technologies.