数字孪生使能技术的不确定性量化和敏感性分析--应用于BISON燃料性能程序
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code
摘要：
为了了解智能验证工具的潜力，美国核管理委员会（NRC）启动了一个面向未来的研究项目，以评估核电应用中机器学习（ML）和人工智能（AI）驱动的数字孪生（DT）的监管可行性。先进事故容忍燃料（ATF）是美国能源部（DOE）的优先重点领域之一。DT框架可以提供改变游戏规则的实用和明智的解决方案，以解决合格的高级ATF的复杂问题。考虑到DT的建模和仿真（M&S）方面的监管立场，不确定性量化和敏感性分析对于DT框架在多标准和风险知情决策方面的成功至关重要。本章介绍了基于ML的不确定性量化和敏感性分析方法，并展示了基于有限元的核燃料性能程序BISON的实际应用。
英文摘要：
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.
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