System identification of a research nuclear reactor versus loss of flow accident using recurrent neural network

Abstract

In this paper, modelling of the Tehran Research Reactor is done using Recurrent Neural Network (RNN) in Loss of Flow Accident (LOFA). TRANS code is calculated as training data mode for each of the scenarios. Supervised recurrent neural network is chosen for modelling and identification system, classified system data and appropriate parameters for modelling function of system have been chosen, then data is classified. In the next step, we choose variant networks to train and compare with each other. Next, an optimised network is chosen according to mean square error parameter and correlation among educational data from TRANS code and network output data. Finally, entrance data related to the unforeseen accident was entered to the system and the predicted results by model and output data of TRANS code were compared. Results demonstrate the appropriate conformity between extraction data of TRANS code and extraction data of the model, which shows appropriate function of the model.