Risk Informed Safety Margin Characterization for Nuclear Reactors Using Dynamic Risk Assesment Methods

该提案考虑与CANDU业主集团合作开发和应用先进的概率风险评估（PRA）方法。虽然应用的目标是核电厂，但这些方法是通用的，可能对进行风险分析的其他经济部门（例如，石化和航空航天）有益。PRA量化了与各种复杂工程系统运行相关的风险，是核电站监管许可的重要组成部分。对风险的准确量化可以对电厂设计的安全性产生重要见解，并可以确定电厂设计以及操作和应急响应程序中的漏洞。近年来，传统的PRA方法得到了扩展，在风险评估中包含了更现实的系统响应预测。这种改进的分析形式-称为动态PRA (D-PRA) -包括在事故场景进展期间物理系统行为的时间依赖性的明确建模，并将人类互动纳入模型。这项工作旨在解决D-PRA框架内目前存在的以下方法缺陷：a)缺乏比较动态和传统PRA方法结果的综合研究，b)在将部分故障和设备延迟和/或操作员响应纳入时间相关模型方面进展甚少，c)需要进一步开发更准确的人为因素模型，特别是在时间敏感的事故情景背景下，如福岛核电站停电（SBO）。D)希望量化工厂安全边际和风险估计之间的关系。此外，在学术文献中没有将这种方法应用于加拿大核电站的应用，因此麦克马斯特大学将与CANDU业主集团合作，使用这些方法进行首次分析。

This proposal considers the development and application of advanced probabilistic risk assessment (PRA) methodologies in collaboration with CANDU Owners Group. While the application is aimed at nuclear power plants, the methods are generic and may have benefit for other economic sectors where risk analyses are performed (e.g., petrochemical and aerospace). PRA quantifies the risk associated with the operation of a broad range of complex engineering systems and forms an important part of the regulatory licensing of nuclear power plants. The accurate quantification of risk can yield important insights into the safety of a plant design and can identify vulnerabilities in plant design and operational and emergency response procedures. In recent years, the traditional methods for PRA have been extended to include more realistic predictions of system response in the risk estimates. This improved form of analysis - called dynamic PRA (D-PRA) - includes explicit modeling of the time-dependence of the physical system behaviour during accident scenario progression, and the inclusion of human interactions into the model. This work has been designed to address the following methodological shortcomings currently present within the D-PRA framework: a) there is a lack of comprehensive study comparing the results of the dynamic and traditional PRA methods, b) there has been little development on incorporating partial failures and delayed equipment and/or operator response into the time-dependent models, c) there is a need for further development of more accurate human factors models, especially in the context of time-sensitive accident scenarios such as a Fukushima-type station blackout (SBO), d) there is a desire to quantify the relationship between plant safety margins and risk estimates. In addition there are no applications in the scholarly literature of such methods being applied to Canadian nuclear power plants so McMaster in collaboration with CANDU Owners Group will perform the first-of-a-kind analysis using these methods.