Adaptive simulation for failure identification in the Advanced Lead Fast Reactor European Demonstrator

The identification undesired or abnormal states of a nuclear power plant is of primary importance for defining accident prevention and mitigation actions. To this aim, computational models and simulators are frequently employed, as they allow to study the system response to different operational conditions. For complex systems like the nuclear power plants, this is in general challenging because the simulation tools are i) high-dimensional; ii) black-box; iii) dynamic and iv) computationally demanding. In this paper, an adaptive simulation framework recently proposed by some of the authors is tailored for the analysis of accident scenarios involving the control system of the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED). The results confirm that the adaptive simulation framework proposed is effective in identifying critical regions of operation with a limited number of calls to the computationally expensive model. The time of occurrence and magnitude of the failures of the components of the control system are identified as key factors to characterize the critical regions. In particular, it is shown that the order of occurrence of the components' failures strongly affects the evolution of the accident scenarios.