Nuclear Engineering and Technology

  • 제49권7호
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  • Pages.1369-1378
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  • 2017
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Adaptive group of ink drop spread: a computer code to unfold neutron noise sources in reactor cores

  • 투고 : 2017.02.18
  • 심사 : 2017.05.30
  • 발행 : 2017.10.25
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초록

The present paper reports the development of a computational code based on the Adaptive Group of Ink Drop Spread (AGIDS) for reconstruction of the neutron noise sources in reactor cores. AGIDS algorithm was developed as a fuzzy inference system based on the active learning method. The main idea of the active learning method is to break a multiple input-single output system into a single input-single output system. This leads to the ability to simulate a large system with high accuracy. In the present study, vibrating absorber-type neutron noise source in an International Atomic Energy Agency-two dimensional reactor core is considered in neutron noise calculation. The neutron noise distribution in the detectors was calculated using the Galerkin finite element method. Linear approximation of the shape function in each triangle element was used in the Galerkin finite element method. Both the real and imaginary parts of the calculated neutron distribution of the detectors were considered input data in the developed computational code based on AGIDS. The output of the computational code is the strength, frequency, and position (X and Y coordinates) of the neutron noise sources. The calculated fraction of variance unexplained error for output parameters including strength, frequency, and X and Y coordinates of the considered neutron noise sources were $0.002682{\sharp}/cm^3s$, 0.002682 Hz, and 0.004254 cm and 0.006140 cm, respectively.

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참고문헌

  1. I. Pazsit, O. Glockler, On the neutron noise diagnostics of PWR control rod vibration: I. Periodic vibrations, Nucl. Sci. Eng. 85 (1984) 167-177.
  2. C. Demaziere, G. Andhill, Identification and localization of absorbers of variable strength in nuclear reactors, Ann. Nucl. Energy 32 (2005) 812-842. https://doi.org/10.1016/j.anucene.2004.12.011
  3. M.M.R. Williams, Random Processes in Nuclear Reactors, Pergamon Press Ltd, Headington Hill Hall, Oxford, 2013.
  4. S.A. Hosseini, N. Vosoughi, Noise source reconstruction using ANN and hybrid methods in VVER-1000 reactor core, Prog. Nucl. Energy 71 (2014) 232-247. https://doi.org/10.1016/j.pnucene.2013.12.007
  5. N.S. Garis, I. Pazsit, U. Sandberg, Determination of PWR control rod position by core physics and neural network methods, Nucl. Technol. 123 (1998) 278-295. https://doi.org/10.13182/NT98-A2899
  6. T. Tambouratzis, M. Antonopoulos-Domis, Instability localization with artificial neural networks (ANNs), Ann. Nucl. Energy 29 (2002) 235-253. https://doi.org/10.1016/S0306-4549(01)00036-6
  7. S.A. Hosseini, N. Vosoughi, Neutron noise simulation by GFEM and unstructured triangle elements, Nucl. Eng. Des. 253 (2012) 238-258. https://doi.org/10.1016/j.nucengdes.2012.08.023
  8. S. Itoh, A fundamental study of neutron spectra unfolding based on the maximum likelihood method, Nucl, Instrum. Methods Phys. Res. Sect. A 251 (1986) 144-155. https://doi.org/10.1016/0168-9002(86)91161-7
  9. L. Zedeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst. Man Cybern. 3 (1973) 28-44.
  10. E.H. Mamdani, Application of fuzzy logic to approximate reasoning using linguistic synthesis, in: Proceedings of the Sixth International Symposium on Multiple-valued Logic, IEEE Computer Society Press, 1976.
  11. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern. SCM-15 (1985) 116-132.
  12. I.E.P. Afrakoti, S.B. Shouraki, F.M. Bayat, M. Gholami, Using a memristor crossbar structure to implement a novel adaptive real-time fuzzy modeling algorithm, Fuzzy Sets Syst. 307 (2016) 115-128.
  13. S.B. Shouraki, N. Honda, Recursive fuzzy modeling based on fuzzy interpolation, J. Adv. Comput. Intell. 3 (1999) 114-125.
  14. M. Murakami, N. Honda, A study on the modeling ability of the IDS method: a soft computing technique using pattern-based information processing, Int. J. Approx. Reason 45 (2007) 470-487. https://doi.org/10.1016/j.ijar.2006.06.022
  15. M. Firouzi, S.B. Shouraki, I.E.P. Afrakoti, Pattern analysis by active learning method classifier, J. Intell. Fuzzy Syst. 26 (2014) 49-62.
  16. Y. Sakurai, A Study of the Learning Control Method Using PBALM-a Nonlinear Modeling Method, PhD, University of Electro-Communications, Tokyo, 2005.
  17. I.E.P. Afrakoti, S.B. Shouraki, B. Haghighat, An optimal hardware implementation for active learning method based on memristor crossbar structures, IEEE Syst. J. 8 (2014) 1190-1199. https://doi.org/10.1109/JSYST.2013.2295963
  18. I.E.P. Afrakoti, A. Ghaffari, S.B. Shouraki, Effective partitioning of input domains for ALM algorithm in pattern recognition and image analysis (PRIA), in: 2013 First Iranian Conference on, IEEE, 2013.
  19. H. Sagha, S.B. Shouraki, H. Beigy, H. Khasteh, E. Enayati, Genetic ink drop spread, in: Intelligent Information Technology Application, IITA'08, Second International Symposium on, IEEE, 2008.
  20. S.A. Hosseini, N. Vosoughi, On a various noise source reconstruction algorithms in VVER-1000 reactor core, Nucl. Eng. Des. 261 (2013) 132-143. https://doi.org/10.1016/j.nucengdes.2013.03.034
  21. S.A. Hosseini, N. Vosoughi, Development of 3D neutron noise simulator based on GFEM with unstructured tetrahedron elements, Ann. Nucl. Energy 97 (2016) 132-141. https://doi.org/10.1016/j.anucene.2016.07.006

피인용 문헌

  1. Neutron spectroscopy with soft computing: unfolding of the neutron energy spectrum using the developed computer code based on Adaptive Group of Ink Drop Spread (AGIDS) vol.14, pp.3, 2017, https://doi.org/10.1088/1748-0221/14/03/p03028
  2. Comparative study between the developed simulators based on the direct and indirect methods for calculation of the energy spectrum of the neutron source vol.14, pp.11, 2019, https://doi.org/10.1088/1748-0221/14/11/p11019
  3. Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency vol.13, pp.6, 2017, https://doi.org/10.2174/2352096512666191127122752