Dynamic probabilistic risk assessment at a design stage for a sodium fast reactor

Sodium Fast Reactor is a good candidate for an application of dynamic probabilist risk assessment due to long mission times and possible system recoveries. In this paper, we present a new approach to construct the dynamic probabilistic model of the Decay Heat Removal (DHR) function of a Sodium Fast Reactor. We use Distributed Stochastic Hybrid Automata concept with the PyCATSHOO modeling tool. The proposed approach allows to construct a dynamic probabilistic model of a SFR DHR function that incorporates the time evolution of physical parameters and dynamic changes in the states of DHR systems (failure or recovery of DHR system components) and presents enough flexibility to be applied at the design stage. (author)