Correlated sampling techniques used in Monte Carlo simulation for risk assessment

Risk assessments in the nuclear industry heavily depend on the study of system availability/reliability and component importance. To do this study, the Monte Carlo simulation method is often a favorite selection since it involves no complex mathematical analysis, especially when systems are so complex or large that deterministic methods are difficult to solve. However, when the importance of components or the time behavior of availability/reliability of a system are required, running conventional Monte Carlo simulation alone can be very tedious and time-consuming. An integrated analysis technique that can be used to obtain the entire information efficiently and precisely in one calculation would be very desired by the system engineers. In this paper, we introduce the correlated sampling techniques to incorporate with conventional Monte Carlo simulation to save engineer's work as well as computing time.