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

Authors: Behzad Salmasian; Gholam Reza Ansarifar; Seyeed Mohammad Mirvakili

Addresses: Department of Nuclear Engineering, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, 81746-73441, Iran ' Department of Nuclear Engineering, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, 81746-73441, Iran ' Nuclear Science and Technology Research Institute (NSTRI), Atomic Energy Organisation of Iran (AEOI), Tehran 14399-51113, Iran

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.

Keywords: TRR; Tehran research reactor; LOFA; loss of flow accident; RNN; recurrent neural network; simulator; TRANS calculation code.

DOI: 10.1504/IJNEST.2018.095697

International Journal of Nuclear Energy Science and Technology, 2018 Vol.12 No.3, pp.283 - 293

Received: 05 Mar 2018
Accepted: 10 Jul 2018

Published online: 16 Oct 2018 *

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