Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal-hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported. (c) 2021 Published by Elsevier Ltd.

Automated summary

This article introduces the shortcomings of traditional event-tree methods and the development of dynamic probabilistic risk assessment techniques, focusing on the framework and capabilities of the RAVEN dynamic PRA tool. It also explains how RAVEN simulates random events, performs uncertainty quantification, and processes the large amount of data generated through sampling.