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In integrated deterministic and probabilistic safety analysis (IDPSA), safe scenarios and prime implicants (PIs) are generated by simulation. In this paper, we propose a novel postprocessing method, which resorts to a risk-based clustering method for identifying Near Misses among the safe scenarios. This is important because the possibility of recovering these combinations of failures within a tolerable grace time allows avoiding deviations to accident and, thus, reducing the downtime (and the risk) of the system. The postprocessing risk-significant features for the clustering are extracted from the following: (i) the probability of a scenario to develop into an accidental scenario, (ii) the severity of the consequences that the developing scenario would cause to the system, and (iii) the combination of (i) and (ii) into the overall risk of the developing scenario. The optimal selection of the extracted features is done by a wrapper approach, whereby a modified binary differential evolution (MBDE) embeds aK-means clustering algorithm. The characteristics of the Near Misses scenarios are identified solving a multiobjective optimization problem, using the Hamming distance as a measure of similarity. The feasibility of the analysis is shown with respect to fault scenarios in a dynamic steam generator (SG) of a nuclear power plant (NPP).

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In this work, we present a transient identification approach that utilizes clustering for retrieving scenarios information from an Integrated Deterministic and Probabilistic Safety Analysis (IDPSA).The approach requires: i) creation of a database of scenarios by IDPSA; ii) scenario post-processing for clustering Prime Implicants (PIs), i.e., minimum combinations of failure events that are capable of leading the system into a fault state, and Near Misses, i.e., combinations of failure events that lead the system to a quasi-fault state; iii) on-line cluster assignment of an unknown developing scenario.In the step ii), we adopt a visual interactive method and risk-based clustering to identify PIs and Near Misses, respectively; in the on-line step iii), to assign a scenario to a cluster we consider the sequence of events in the scenario and evaluate the Hamming similarity to the sequences of the previously clustered scenarios. The feasibility of the analysis is shown with respect to the accidental scenarios of a dynamic Steam Generator (SG) of a NPP.

In this work, a sensitivity analysis framework is presented to identify the relevant input variables of a severe accident code, based on an incremental Bayesian ensemble updating method. The proposed methodology entails: i) the propagation of the uncertainty in the input variables through the severe accident code; ii) the collection of bootstrap replicates of the input and output of limited number of simulations for building a set of Finite Mixture Models (FMMs) for approximating the probability density function (pdf) of the severe accident code output of the replicates; iii) for each FMM, the calculation of an ensemble of sensitivity measures (i.e., input saliency, Hellinger distance and Kullback-Leibler divergence) and the updating when a new piece of evidence arrives, by a Bayesian scheme, based on the Bradley-Terry model for ranking the most relevant input model variables. An application is given with respect to a limited number of simulations of a MELCOR severe accident model describing the fission products release in the LP-FP-2 experiment of the Loss Of Fluid Test (LOFT) facility, which is a scaled-down facility of a pressurized water reactor (PWR).