Automatic accident sequence generation using optimized simulations for dynamic probabilistic safety assessment

Abstract

In the realm of dynamic probabilistic safety assessment, dynamic event trees (DETs) have been used to represent dynamic scenarios by branching at user-specified times or when an action is required by the operator and/or the systems to capture sequences of events based on their timing. While this approach provides a realistic and mechanistic analysis, it often results in an excessive number of branches, which reduces the explainability of the accident sequences and the visibility of the DET in comparison to traditional event trees based on representative scenarios. To address these challenges, this study proposes a method named DRAGON (Dynamic Risk Assessment through automatic accident sequence Generation using Optimized simulations for Nuclear power plants) for automatically generating accident sequences using optimized simulations for dynamic risk assessment. The proposed approach employs an optimized simulation algorithm to identify the limit surface between success and failure scenarios, which reduces the required simulations. Then a newly developed algorithm as a part of DRAGON provides the ability to analyze these simulations and control the complexity of the DET to present accident sequences in an interpretable form. To demonstrate the practicality and the effectiveness of the approach, two case studies on a loss of coolant accident were conducted, and comparisons with other data analysis methods were explored.