Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of LOCA Break Size Using Cascaded Fuzzy Neural Networks

  • Choi, Geon Pil (Department of Nuclear Engineering, Chosun University) ;
  • Yoo, Kwae Hwan (Department of Nuclear Engineering, Chosun University) ;
  • Back, Ju Hyun (Department of Nuclear Engineering, Chosun University) ;
  • Na, Man Gyun (Department of Nuclear Engineering, Chosun University)
  • Received : 2016.07.22
  • Accepted : 2016.11.04
  • Published : 2017.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

Keywords

References

  1. M.G. Na, S.H. Shin, D.W. Jung, S.P. Kim, J.H. Jeong, B.C. Lee, Estimation of break location and size for loss of coolant accidents using neural networks, Nucl. Eng. Des. 232 (2004) 289-300. https://doi.org/10.1016/j.nucengdes.2004.06.007
  2. J. Garvey, D. Garvey, R. Seibert, J.W. Hines, Validation of online monitoring techniques to nuclear plant data, Nucl. Eng. Technol. 39 (2007) 149-158. https://doi.org/10.5516/NET.2007.39.2.149
  3. J.W. Hines, D.J. Wrest, R.E. Uhrig, Signal validation using an adaptive neural fuzzy inference system, Nucl. Technol. 119 (1997) 181-193. https://doi.org/10.13182/NT97-A35385
  4. M.G. Na, A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPR, J. Korean Nucl. Soc. 33 (2001) 483-497.
  5. E.B. Bartlett, R.E. Uhrig, Nuclear power plant diagnostics using an artificial neural network, Nucl. Technol. 97 (1992) 272-281. https://doi.org/10.13182/NT92-A34635
  6. A. Gofuku, H. Yoshikawa, S. Hayashi, K. Shimizu, J. Wakabayashi, Diagnostic techniques of a small-break lossof-coolant accident at a pressurized water reactor plant, Nucl. Technol. 81 (1988) 313-332. https://doi.org/10.13182/NT88-A16054
  7. M. Marseguerra, E. Zio, Fault diagnosis via neural networks: the Boltzmann machine,, Nucl. Sci. Eng. 117 (1994) 194-200. https://doi.org/10.13182/NSE94-A28534
  8. Y.G. No, J.H. Kim, D.H. Lim, K.I. Ahn, Monitoring severe accidents using AI techniques, Nucl. Eng. Technol. 44 (2012) 393-404. https://doi.org/10.5516/NET.04.2012.512
  9. M.G. Na, H.Y. Yang, D.H. Lim, A soft-sensing model for feedwater flow rate using fuzzy support vector regression, Nucl. Eng. Technol 40 (2008) 69-76. https://doi.org/10.5516/NET.2008.40.1.069
  10. S.H. Park, D.S. Kim, J.H. Kim, M.G. Na, Prediction of the reactor vessel water level using fuzzy neural networks in severe accident circumstances of NPPs, Nucl. Eng. Technol. 46 (2014) 373-380. https://doi.org/10.5516/NET.04.2013.087
  11. S.H. Park, J.H. Kim, K.H. Yoo, M.G. Na, Smart sensing of the RPV water level in NPP severe accidents using a GMDH algorithm, IEEE Trans. Nucl. Sci. 61 (2014) 931-938. https://doi.org/10.1109/TNS.2014.2305444
  12. D.Y. Kim, K.H. Yoo, M.G. Na, Estimation of minimum DNBR using cascaded fuzzy neural networks, IEEE Trans. Nucl. Sci. 62 (2015) 1849-1856. https://doi.org/10.1109/TNS.2015.2457446
  13. E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man. Mach. Stud. 7 (1975) 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
  14. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man. Cybern. 1 (1985) 116-132.
  15. J.C. Duan, F.L. Chung, Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning, IEEE Trans. Fuzzy Syst. 9 (2001) 293-306. https://doi.org/10.1109/91.919250
  16. D.Y. Kim, K.H. Yoo, G.P. Choi, J.H. Back, M.G. Na, Reactor vessel water level estimation during severe accidents using casecaded fuzzy neural networks, Nucl. Eng. Technol. 48 (2016) 702-710. https://doi.org/10.1016/j.net.2016.02.002
  17. I. Lindholm, E. Pekkarinen, H. Sjovall, Evaluation of reflooding effects on an overheated boiling water reactor core in a small steam-line break accident using MAAP, MELCOR, and SCDAP/RELAP5 computer codes, Nucl. Technol. 112 (1995) 42-57. https://doi.org/10.13182/NT95-A15850
  18. C. Allison, Comparison between MAAP, MELCOR and SCDAP/RELAP5, in: Proceedings of the Workshop on Severe Accident Research, Japan (SARJ-97), 1998, pp. 396-401.
  19. M.G. Na, W.S. Park, D.H. Lim, Detection and diagnostics of loss of coolant accidents using support vector machines, IEEE Trans. Nucl. Sci. 55 (2008) 628-636. https://doi.org/10.1109/TNS.2007.911136

Cited by

  1. Identification of LOCA and Estimation of Its Break Size by Multiconnected Support Vector Machines vol.64, pp.10, 2017, https://doi.org/10.1109/tns.2017.2743098
  2. Development of a NO x emission model with seven optimized input parameters for a coal-fired boiler vol.19, pp.4, 2017, https://doi.org/10.1631/jzus.a1600787
  3. Neural-based time series forecasting of loss of coolant accidents in nuclear power plants vol.160, pp.None, 2017, https://doi.org/10.1016/j.eswa.2020.113699
  4. Artificial intelligence in nuclear industry: Chimera or solution? vol.278, pp.None, 2017, https://doi.org/10.1016/j.jclepro.2020.124022