Processing dynamic scenarios from a reliability analysis of a nuclear power plant digital instrumentation and control system

This paper illustrates a method for processing accident scenarios generated in a dynamic reliability analysis of a Nuclear Power Plant (NPP) equipped with digital Instrumentation and Control (I&C).The method is based on a Fuzzy C-Means clustering algorithm for classification, which takes into account not only the system states reached at the end of the scenarios but also the timing and magnitude of the occurred failure events, and the characteristics of the process evolution.An illustrative case study is considered, regarding the fault scenarios of the digital I&C of the Lead–Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). A SIMULINK model of the system has been embedded within a Monte Carlo (MC) sampling procedure for injecting faults at random times and of random magnitudes. The accident scenarios thereby generated are classified on the basis of three different system end states, which relate to the value reached by the diathermic oil secondary coolant temperature with respect to maximum and minimum safety threshold values set to avoid primary coolant thermal shocks and degradation of the oil physical and chemical properties.