The cell-to-boundary method in the frame of memorization-based Monte Carlo algorithms. A new computational improvement in dynamic reliability

Dynamic reliability aims at estimating failure risks associated with very rare scenarios, while accounting for the system dynamic evolution. In a Monte Carlo game devised for this purpose, the most time consuming operation consists in performing these dynamic calculations, which are repeated in thousands of histories. In order to save computer resources, the idea of memorizing information on the dynamic trajectories before the simulation was investigated. Two approaches were propounded: the cell-to-boundary (CTB) method, and algorithms based on the memorization of the most probable evolution (MPE) from each initial state. This paper presents a way to combine both methods, in order to further reduce the numerical workload of the simulation. A memorization of second-order MPEs is also propounded, to better investigate transients following the failure of a control means. These techniques are illustrated on the previously defined application of a PWR pressurizer.