U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (DOE) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the DOE/ NRC. DTs have the potential to transform the nuclear energy sector in the coming years by incorporating risk-informed decision-making into the Accelerated Fuel Qualification (AFQ) process for ATF. A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. However, novel ATF technology suffers from a couple of challenges, such as (i) Data unavailability; (ii) Lack of data, missing data; and (iii) Model uncertainty. These challenges must be resolved to gain the trust in DT framework development. In addition, DT-enabling technologies consist of three major areas: (i) modeling and simulation (M&S), covering uncertainty quantification (UQ), sensitivity analysis (SA), data analytics through ML/AI, physics-based models, and data-informed modeling, (ii) Advanced sensors/instrumentation, and (iii) Data management. UQ and SA are important segments of DT-enabling technologies to ensure trustworthiness, which need to be implemented to meet the DT requirement. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, UQ and SA are paramount to the success of DT framework in terms of multi-criteria and risk-informed decision-making. In this study, the adaptability of polynomial chaos expansion (PCE) based UQ/SA in a non-intrusive method in BISON was investigated to ensure M&S aspects of the AFQ for ATF. This study introduces the ML-based UQ and SA methods while exhibiting actual applications to the finite element-based nuclear fuel performance code.
翻译:US. 核监管委员会(核监委)和美国能源部(能源部)启动了一个以未来为重点的研究项目,以评估核应用机器学习(ML)和人工智能驱动的数字双体(DTs)的监管可行性。高级事故容忍燃料(ATF)是DO/NRC的优先重点领域之一。DT有可能在未来几年里通过将风险知情决策纳入加速燃料认证的燃料资格(AFQ)进程来改造核能部门。 一项DT框架可以为符合资格的高级ATF这一复杂问题提供改变游戏的、实际的和知情的解决方案。然而,新的ATF技术面临若干挑战,如:(一) 数据缺乏;(二) 缺乏、数据缺失;(三) 模型不确定性。必须解决这些挑战,以获得对DT框架开发的信任。 此外,基于DT的测试技术包括三个主要领域:(一) 模型和模拟(M&S) 包括不确定性量化(UQ)、敏感性分析(SA)、数据分析(SA) 数据管理部分,通过数据测试(SDDR) 数据测试(S) 数据测试(S) 基础研究(SAL) 基础研究(IL)。