  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 and 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.
