MONTE-CARLO ESTIMATION OF GENERALIZED UNRELIABILITY IN PROBABILISTIC DYNAMICS .1. APPLICATION TO A PRESSURIZED-WATER REACTOR PRESSURIZER

Probabilistic dynamics offers a general Markovian framework for a dyna mic treatment of reliability. Monte Carlo simulation appears to be a p owerful and flexible tool to deal with the high dimensionality of real istic applications. Yet an analog game turns out to be ineffective for two main reasons: Very rare events leading to failures are not sample d enough to obtain a good statistical accuracy, and the equations of t he dynamics have to be integrated all along each history, which result s in very large computation times. Recent improvements in Monte Carlo simulation applied to probabilistic dynamics allow a much faster and m ore precise estimation of the unreliability of large systems, and they are illustrated on a pressurized water reactor pressurizer.