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

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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.