Identification of nuclear transients via optimized fuzzy clustering

Scenario Grouping and Classification Methodology for Postprocessing of Data Generated by Integrated Deterministic-Probabilistic Safety Analysis

Integrated Deterministic-Probabilistic Safety Assessment (IDPSA) combines deterministic model of a nuclear power plant with a method for exploration of the uncertainty space. Huge amount of data is generated in the process of such exploration. It is very difficult to “manually” process and extract from such data information that can be used by a decision maker for risk-informed characterization, understanding, and eventually decision making on improvement of the system safety and performance. Such understanding requires an approach for interpretation, grouping of similar scenario evolutions, and classification of the principal characteristics of the events that contribute to the risk. In this work, we develop an approach for classification and characterization of failure domains. The method is based on scenario grouping, clustering, and application of decision trees for characterization of the influence of timing and order of events. We demonstrate how the proposed approach is used to classify scenarios that are amenable to treatment with Boolean logic in classical Probabilistic Safety Assessment (PSA) from those where timing and order of events determine process evolution and eventually violation of safety criteria. The efficiency of the approach has been verified with application to the SARNET benchmark exercise on the effectiveness of hydrogen management in the containment.

Identification and classification of dynamic event tree scenarios via possibilistic clustering: application to a steam generator tube rupture event

This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a steam generator tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators' crew model, which simulates their cognitive and procedures-guided responses. A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was "discovered" by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.

A fuzzy decision tree for fault classification

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.