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.

翻译：为满足下一代核系统中建立集成人工智能/机器学习（AI/ML）的数字孪生（DT）技术的迫切需求，需推进建模方法与仿真代码的优化升级。模型复杂度的提升导致不确定性量化需要消耗大量计算资源。针对这一挑战，本文提出一种基于多项式混沌展开的数据驱动非侵入式不确定性量化方法，作为有限元分析燃料性能代码BISON中的高效策略。研究制备了不同燃料模型及SiC/SiC包壳材料以验证所提方法。通过量化四种独立不确定输入变量对系统输出的影响，每个模型仅需不足100次BISON仿真即可完成分析。该方法不仅加速了建模与仿真任务，更显著提升了数字孪生赋能技术开发过程中的可靠性。