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Abstract TA193Abstract + Presentation
Simulation-Based Probabilistic Reliability Techniques for Complex Nuclear Safety Systems
Simulation-Based Probabilistic Reliability Techniques for Complex Nuclear Safety Systems
Modern nuclear power plants increasingly rely on complex digital instrumentation and control (I&C) systems whose reliability characteristics are difficult to evaluate using traditional analytical reliability techniques. Conventional methods such as fault trees and reliability block diagrams often rely on simplifying assumptions that may not adequately capture system dynamics, complex dependencies, or stochastic behaviors present in advanced safety systems.
A simulation-based probabilistic reliability framework will be designed to evaluate the performance and failure behavior of complex nuclear safety systems. The approach integrates stochastic simulation methods—primarily Monte Carlo–based techniques—with probabilistic reliability modeling to estimate system availability, failure probabilities, and risk metrics under realistic operational scenarios. The methodology enables explicit modeling of component interactions, time-dependent failures, common cause events, and maintenance or testing intervals, which are often challenging to represent using classical reliability techniques.
The proposed framework will be applied to representative nuclear safety architectures to demonstrate its capability in evaluating system reliability and identifying dominant contributors to system unavailability. Simulation results will be compared with conventional analytical reliability calculations to highlight differences and potential improvements in modeling fidelity. The analysis will also illustrate how simulation-based techniques can support risk-informed decision-making, optimize surveillance and maintenance strategies, and enhance reliability assessments for modern digital safety systems.
The results goal is to demonstrate that simulation-based probabilistic methods provide a flexible and powerful toolset for assessing the reliability of increasingly complex nuclear safety systems and can complement traditional probabilistic risk assessment approaches used in the nuclear industry.
← Check another abstractModern nuclear power plants increasingly rely on complex digital instrumentation and control (I&C) systems whose reliability characteristics are difficult to evaluate using traditional analytical reliability techniques. Conventional methods such as fault trees and reliability block diagrams often rely on simplifying assumptions that may not adequately capture system dynamics, complex dependencies, or stochastic behaviors present in advanced safety systems.
A simulation-based probabilistic reliability framework will be designed to evaluate the performance and failure behavior of complex nuclear safety systems. The approach integrates stochastic simulation methods—primarily Monte Carlo–based techniques—with probabilistic reliability modeling to estimate system availability, failure probabilities, and risk metrics under realistic operational scenarios. The methodology enables explicit modeling of component interactions, time-dependent failures, common cause events, and maintenance or testing intervals, which are often challenging to represent using classical reliability techniques.
The proposed framework will be applied to representative nuclear safety architectures to demonstrate its capability in evaluating system reliability and identifying dominant contributors to system unavailability. Simulation results will be compared with conventional analytical reliability calculations to highlight differences and potential improvements in modeling fidelity. The analysis will also illustrate how simulation-based techniques can support risk-informed decision-making, optimize surveillance and maintenance strategies, and enhance reliability assessments for modern digital safety systems.
The results goal is to demonstrate that simulation-based probabilistic methods provide a flexible and powerful toolset for assessing the reliability of increasingly complex nuclear safety systems and can complement traditional probabilistic risk assessment approaches used in the nuclear industry.