A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

Abstract

Nowadays, the emerging digital technologies and digitalization trend in safety‐critical industrial process systems are bringing great opportunities for system performance improvements. However, new big challenges are also encountered in the reliability and safety evaluation of large complex industrial process systems due to its multi‐state, multi‐phase dynamic interactions, resilience on software and inter‐dependencies among digital components. The objectives of this study are i) to present an introductory overview of a hybrid computing framework as a supplementary to conventional fault tree analysis toolkit for risk‐oriented reliability analysis in dynamic probabilistic safety assessment context; ii) to illustrate how to combine the three methods of DDET, Markov/CCMT, and GO‐FLOW for integrated risk solutions by a case study of small‐break LOCA in nuclear power plants. Within the hybrid computing framework, the DDET model is implemented based on graph‐based search and sequence diagram refactoring by linking with Markov/CCMT and GO‐FLOW solver for branch probability estimation. The dynamic event tree model is adopted to represent the accident sequence of small‐break LOCA, where the heading events of system failure of digital RPS and phased‐mission ECCS in realization of safety‐critical functions of reactivity control and emergency core cooling are respectively modeled and analyzed by Markov chain and GO‐FLOW method. The demonstration results show that the failure analysis of complex dynamic process interactions together with time‐dependent mission reliability analysis of safety systems involved in accident prevention and mitigation can be easily implemented with accurate modeling and fast evaluation within the hybrid integration platform. The core algorithms and principles implemented for dynamic risk scenarios development by DDET, modeling, and analysis of dynamic process interactions by Markov/CCMT, time‐dependent and multi‐phase mission reliability analysis by GO‐FLOW as well as their integration to provide comprehensive solutions for the application of dynamic reliability in risk assessment are also discussed as open problems for future research.

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

“…The ET‐FT model can identify logical relationships among risk factors (i.e., HEs, machine failures and abnormal environments) contributing to accident and can effectively achieve the risk assessment of the system 30 . The structure of the ET‐FT model is shown in Figure 2.…”

“…At the same time, they used the quantification of FTs to provide the probability of passing along each ET branch. The Boolean logic‐based ET‐FT approaches have proven to be very effective in analyzing risks in complex systems with deterministic causal relationships 30 . However, HEs are uncertain and may be triggered with different probabilities in different cases.…”

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

“…Jiang et al 4 proposed hybrid FD and diagnosis approaches. This approach, integrated with safety and risk assessment (RA), is being explored in various domains, including nuclear power plants.…”

“…GO-FLOW focuses on emergency planning, helping to develop efficient strategies for managing crises effectively. However, in closed-loop experiments like BR setups, where events are tightly controlled and interconnected, the applicability of the methods used in ref ( 4 ) might be limited. This is because these techniques are primarily designed for open-loop experiments where events occur more independently and unpredictably.…”

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes

“…The “curse of dimensionality” refers to the exponential explosive growth of the system state space and the scale of the Markov model with the number of system components [ 20 ]. The main research on the “curse of dimensionality” in reliability assessments based on the Markov method may be broadly divided into two categories: (1) The integration of other methodologies: In practice, Markov methods often are used in conjunction with other methods in reliability assessments of complex systems [ 21 , 22 ]. For instance, Abdulmunem employed a continuous-time Markov method in conjunction with the fault tree method to address the reliability modeling of complex systems [ 23 ].…”