Prediction of Leak Flow Rate Using Fuzzy Neural Networks in Severe Post-LOCA Circumstances

Abstract

Providing information about the leak flow rate caused by a loss-of-coolant accident (LOCA) to nuclear power plant (NPP) operation personnel is a key to the management and mitigation of severe post-LOCA circumstances at NPPs where active safety injection systems do not actuate. The leak flow rate is a function of break size, differential pressure (i.e., difference between internal and external reactor vessel pressure), temperature, and so on. In this study, the break position and size were first identified and predicted, and then, the leak flow rate was predicted using a fuzzy neural network (FNN). The FNN was developed using training data and validated using independent test data. The data were generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The data for training the FNN model were selected among the acquired data using the subtractive clustering method, and FNN performance was improved. The developed FNN model was sufficiently accurate to be used for predicting leak flow rate, which is useful information for managing severe post-LOCA situations.