Nuclear Engineering and Technology

  • 제47권2호
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  • Pages.139-147
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  • 2015
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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PREDICTION OF HYDROGEN CONCENTRATION IN CONTAINMENT DURING SEVERE ACCIDENTS USING FUZZY NEURAL NETWORK

  • KIM, DONG YEONG (Department of Nuclear Engineering, Chosun University) ;
  • KIM, JU HYUN (Department of Nuclear Engineering, Chosun University) ;
  • YOO, KWAE HWAN (Department of Nuclear Engineering, Chosun University) ;
  • NA, MAN GYUN (Department of Nuclear Engineering, Chosun University)
  • 투고 : 2014.10.08
  • 심사 : 2014.12.05
  • 발행 : 2015.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, severe accidents in nuclear power plants (NPPs) have become a global concern. The aim of this paper is to predict the hydrogen buildup within containment resulting from severe accidents. The prediction was based on NPPs of an optimized power reactor 1,000. The increase in the hydrogen concentration in severe accidents is one of the major factors that threaten the integrity of the containment. A method using a fuzzy neural network (FNN) was applied to predict the hydrogen concentration in the containment. The FNN model was developed and verified based on simulation data acquired by simulating MAAP4 code for optimized power reactor 1,000. The FNN model is expected to assist operators to prevent a hydrogen explosion in severe accident situations and manage the accident properly because they are able to predict the changes in the trend of hydrogen concentration at the beginning of real accidents by using the developed FNN model.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF)

참고문헌

  1. 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
  2. M.G. Na, A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPRT, Journal of the Korean Nucl. Soc. 33 (2001) 483-497.
  3. 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
  4. 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
  5. M. Marseguerra, E. Zio, Fault diagnosis via neural networks: The Boltzmann machine, Nucl. Sci. Technol. 117 (1994) 194-200.
  6. Y.G. No, J.H. Kim, M.G. Na, 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
  7. A. Gofuku, H. Yoshikawa, S. Hayashi, K. Shimizu, J. Wakabayashi, Diagnostic techniques of a small-break loss-of-coolant accident at a pressurized water reactor plant, Nucl. Technol. 81 (1988) 313-332. https://doi.org/10.13182/NT88-A16054
  8. M.G. Na, S.M. Lee, S.H. Shin, D.W. Jung, S.P. Kim, J.H. Jeong, B.C. Lee, Prediction of major transient scenarios for severe accidents of nuclear power plants, IEEE Trans. Nucl. Sci. 51 (2004) 313-321. https://doi.org/10.1109/TNS.2004.825090
  9. S.W. Cheon, S.H. Chang, Application of neural networks to a connectionist expert system for transient identification in nuclear power plants, Nucl. Technol. 102 (1993) 177-191. https://doi.org/10.13182/NT93-A34815
  10. Y. Bartal, J. Lin, R.E. Uhrig, Nuclear power plant transient diagnostics using artificial neural networks that allow don't-know classifications, Nucl. Technol. 110 (1995) 436-449. https://doi.org/10.13182/NT95-A35112
  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. 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
  13. 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
  14. J.S. Roger Jang, C.T.Sun, Functionalequivalencebetweenradial basis function networks and fuzzy inference systems, Inst. Electr. Electron. Eng. Trans. Neural Netw. 4 (1993) 156-159.
  15. MAAP4 Modular Accident Analysis Program for LWR Power Plants User's Manual., Electric Power Research Institute, Palo Alto (1994-2005)
  16. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, Inst. Electr. Electron. Eng. Trans. Syst. Man. Cybern. SMC-15 (1985) 116-132.
  17. 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
  18. S.H. Lee, Y.G. No, M.G. Na, K.I. Ahn, S.Y. Park, Diagnostics of loss of coolant accidents using SVC and GMDH models, IEEE Trans. Nucl. Sci. 58 (2011) 267-276. https://doi.org/10.1109/TNS.2010.2091972
  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
  20. 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. Design 232 (2004) 289-300. https://doi.org/10.1016/j.nucengdes.2004.06.007

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