Table of Contents Author Guidelines Submit a Manuscript
Journal of Engineering
Volume 2014 (2014), Article ID 798160, 7 pages
http://dx.doi.org/10.1155/2014/798160
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.

Linked References

  1. G. L. Kulcinski, “Non-electric applications of fusion energy—an important precursor to commercial electric power,” Fusion Technology, vol. 34, no. 3, pp. 477–483, 1998. View at Google Scholar · View at Scopus
  2. R. P. Ashley and the UW IEC Team, “Experimental progress in 2003 of the UW IEC facility,” in Proceedings of the 6th U.S.-Japan IEC Workshop, Tokyo, Japan, October 2003.
  3. R. P. Ashley, G. L. Kulcinski, J. F. Santarius, S. K. Murali, G. Piefer, and R. Radel, “Steady-state D3He proton production in an IEC fusion device,” Fusion Technology, vol. 39, no. 2, pp. 546–551, 2001. View at Google Scholar · View at Scopus
  4. C. C. Dobson and I. Hrbud, “Electron density and two-channel neutron emission measurements in steady-state spherical inertial-electrostatically confined plasmas, with review of the one-dimensional kinetic model,” Journal of Applied Physics, vol. 96, no. 1, pp. 94–108, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Yoshikawa, K. Masuda, T. Takamatsu et al., “Research and development on a compact discharge-driven D-D fusion neutron source for explosive detection,” in Proceedings of the 2nd Joint International Conference on Sustainable Energy and Environment (SEE '06), Bangkok, Thailand, November 2006.
  6. T. Takamatsu, K. Masuda, T. Kyunai, H. Toku, and K. Yoshikawa, “Inertial electrostatic confinement fusion device with an ion source using a magnetron discharge,” Nuclear Fusion, vol. 46, no. 1, pp. 142–148, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. V. Damideh, A. Sadighzadeh, A. Koohi et al., “Experimental study of the Iranian inertial electrostatic confinement fusion device as a continuous neutron generator,” Journal of Fusion Energy, vol. 31, no. 2, pp. 109–111, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. H. Miley, Y. Yang, J. Webber, Y. Shaban, and H. Momota, “RF ion source-driven IEC design and operation,” Fusion Science and Technology, vol. 47, no. 4, pp. 1233–1237, 2005. View at Google Scholar · View at Scopus
  9. G. H. Miley, Y. Shaban, and Y. Yang, “RF ion gun injector in support of fusion ship II research and development,” AIP Conference Proceedings, vol. 699, no. 1, p. 406, 2004. View at Google Scholar
  10. A. L. Wehmeyer, R. F. Radel, and G. L. Kulcinski, “Optimizing neutron production rates from DD fusion in an inertial electrostatic confinement device,” in Proceedings of the 6th ANS Topical Meeting on Fusion Energy, Madison, Wis, USA, September 2004.
  11. C. C. Dietrich, Improving particle confinement in inertial electrostatic fusion for spacecraft power and propulsion [Ph.D. thesis], MIT University, 2007.
  12. E. H. Ebrahimi, R. Amrollahi, A. Sadighzadeh et al., “The influence of cathode voltage and discharge current on neutron production rate of inertial electrostatic confinement fusion (IR-IECF),” Journal of Fusion Energy, vol. 32, no. 1, pp. 62–65, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. H. K. Cigizoglu, “Estimation, forecasting and extrapolation of river flows by artificial neural networks,” Hydrological Sciences Journal, vol. 48, no. 3, pp. 349–362, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Liu and J. Liu, “Seismic-controlled nonlinear extrapolation of well parameters using neural networks,” Geophysics, vol. 63, no. 6, pp. 2035–2041, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. Ö. Kişi, “Streamflow forecasting using different artificial neural network algorithms,” Journal of Hydrologic Engineering, vol. 12, no. 5, pp. 532–539, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. C. M. Salgado, L. E. B. Brandão, R. Schirru, C. M. N. A. Pereira, A. X. da Silva, and R. Ramos, “Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network,” Applied Radiation and Isotopes, vol. 67, no. 10, pp. 1812–1818, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Rahimi-Ajdadi and Y. Abbaspour-Gilandeh, “Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption,” Measurement, vol. 44, no. 10, pp. 2104–2111, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. F. J. de Cos Juez, M. A. Suárez-Suárez, F. Sánchez Lasheras, and A. Murcia-Mazón, “Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women,” Mathematical and Computer Modelling, vol. 54, no. 7-8, pp. 1665–1670, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Ebrahimzadeh and A. Khazaee, “Detection of premature ventricular contractions using MLP neural networks: a comparative study,” Measurement, vol. 43, no. 1, pp. 103–112, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. A. R. Gallant and H. White, “On learning the derivatives of an unknown mapping with multilayer feedforward networks,” Neural Networks, vol. 5, pp. 129–138, 1992. View at Google Scholar
  21. J. G. Taylor, Neural Networks and Their Applications, John Wiley & Sons, West Sussex, UK, 1996. View at MathSciNet
  22. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994. View at Publisher · View at Google Scholar · View at Scopus
  23. B.-H. Juang and S. Katagiri, “Discriminative learning for minimum error classification,” IEEE Transactions on Signal Processing, vol. 40, no. 12, pp. 3043–3054, 1992. View at Publisher · View at Google Scholar · View at Scopus