论文标题
数字双胞胎启用技术的不确定性量化和灵敏度分析：野牛燃料绩效代码的应用
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code
论文作者
论文摘要
为了了解智能验证工具的潜力，美国核监管委员会（NRC）启动了一项以未来的研究项目，以评估机器学习（ML）和人工智能（AI）驱动的数字双胞胎（DTS）的监管可行性，以用于核电应用。高级事故耐受燃料（ATF）是美国能源部（DOE）的优先重点领域之一。 DT框架可以为合格的高级ATF提供改变游戏规则但实用的知情解决方案。考虑到DT的建模和仿真（M＆S）方面的调节角度，不确定性量化和灵敏度分析对于DT框架在多标准和风险信息的决策方面的成功至关重要。本章介绍了基于ML的不确定性量化和灵敏度分析方法，同时向有限的基于有限元的核燃料性能代码野牛展示了实际应用。
To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) 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 power applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the U.S. Department of Energy (DOE). A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, uncertainty quantification and sensitivity analysis are paramount to the DT framework's success in terms of multi-criteria and risk-informed decision-making. This chapter introduces the ML-based uncertainty quantification and sensitivity analysis methods while exhibiting actual applications to the finite element-based nuclear fuel performance code BISON.
