论文标题
AI驱动的非侵入性不确定性量化用于数字双胞胎技术的高级核燃料
AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
论文作者
论文摘要
为了应对在下一代核系统中建立AI/ML集成数字双（DT）技术的迫切需求，需要在建模方法和仿真代码方面的进步。模型的复杂性提高需要大量的计算资源来量化其不确定性。为了应对这一挑战，在基于有限元分析的基于有限元分析的燃料绩效代码野牛中引入了通过多项式混乱扩展的数据驱动的非侵入不确定性量化方法。与SIC/SIC覆层材料以及燃料的模型和燃料的模型一起准备演示所提出的方法。量化了四个独立的不确定输入变量对系统输出的影响，每个模型需要少于100个野牛模拟。这种方法不仅加速了建模和仿真任务，而且还提高了DT增强技术开发的可靠性。
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.
