Table of Contents Author Guidelines Submit a Manuscript
Journal of Engineering
Volume 2014, Article ID 798160, 7 pages
Research Article

Prediction of Neutron Yield of IR-IECF Facility in High Voltages Using Artificial Neural Network

1Plasma and Fusion Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
2Faculty of New Science and Technology, University of Isfahan, Isfahan, Iran
3Department of Radiation Application, Shahid Beheshti University, GC, Tehran, Iran
4Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
5Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran

Received 18 July 2014; Revised 2 November 2014; Accepted 2 November 2014; Published 16 December 2014

Academic Editor: Jyh-Hong Chou

Copyright © 2014 A. Sadighzadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial neural network (ANN) is applied to predict the number of produced neutrons from IR-IECF device in wide discharge current and voltage ranges. Experimentally, discharge current from 20 to 100 mA had been tuned by deuterium gas pressure and cathode voltage had been changed from −20 to −82 kV (maximum voltage of the used supply). The maximum neutron production rate (NPR) of 1.46 × 107 n/s had occurred when the voltage was −82 kV and the discharge current was 48 mA. The back-propagation algorithm is used for training of the proposed multilayer perceptron (MLP) neural network structure. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data. Results show that NPR of 1.855 × 108 n/s can be achieved in voltage and current of 125 kV and 45 mA, respectively. This prediction shows 52% increment in maximum voltage of power supply. Also, the optimum discharge current can increase 1270% NPR.