The cell-to-boundary method in Monte Carlo-based dynamic PSA

In recent years, experts in Probabilistic Safety Assessment (PSA) have begun to consider the possibility of improving the safety analysis of a plant by explicitly taking into account the effects of the systemsʹ dynamic evolution. In this case, the system under study should be characterized in terms of its hardware stochastic features, of the values that the internal process variables take during the evolution, and in terms of the influence of these physical variables on the stochastic failure probabilities. Such a dynamic analysis entails the implementation of appropriate mathematical models describing the plantʹs behaviour. s context, Monte Carlo methods seem to be particularly well suited for coupling the dynamic and stochastic aspects of the analysis. Indeed, the simulation structure, typical of Monte Carlo methods, allows us to follow each sequence separately, thus making it possible to include both the stochastic behaviour and the time-dependent response of the system. present paper, we propose an approach for the approximate integration of the equations which describe the process evolution in a dynamic PSA analysis. The method is based on replacing the state space continuum with a discrete grid of nodes, which represent the basis for an interpolation procedure suitable to describe the evolution of the point representative of the state of the system. oposed method is applied to evaluate the availability characteristics of a simple dynamic system often considered in literature. Since the componentsʹ stochastic transition rates are assumed to depend on the time-dependent temperature of the fluid, the process is semi-Markovian.