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Journal of the Korean Nuclear Society = 원자력학회지 v.36 no.5, 2004년, pp.420 - 429   피인용횟수: 1

Estimation of the Nuclear Power Peaking Factor Using In-core Sensor Signals

Na, Man-Gyun    (Chosun University   ); Jung, Dong-Won    (Chosun University   ); Shin, Sun-Ho    (Chosun University   ); Lee, Ki-Bog    (Korea Atomic Energy Research Institute   ); Lee, Yoon-Joon    (Cheju National University  );
  • 초록

    The local power density should be estimated accurately to prevent fuel rod melting. The local power density at the hottest part of a hot fuel rod, which is described by the power peaking factor, is more important information than the local power density at any other position in a reactor core. Therefore, in this work, the power peaking factor, which is defined as the highest local power density to the average power density in a reactor core, is estimated by fuzzy neural networks using numerous measured signals of the reactor coolant system. The fuzzy neural networks are trained using a training data set and are verified with another test data set. They are then applied to the first fuel cycle of Yonggwang nuclear power plant unit 3. The estimation accuracy of the power peaking factor is 0.45% based on the relative $2_{\sigma}$ error by using the fuzzy neural networks without the in-core neutron flux sensors signals input. A value of 0.23% is obtained with the in-core neutron flux sensors signals, which is sufficiently accurate for use in local power density monitoring.


  • 주제어

    COLSS .   fuzzy neural network .   in-core flux sensor .   local power density .   power peaking factor.  

  • 참고문헌 (15)

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    4. Final Safety Analysis Report for Wolsung Unit 1, Korea Electric Power Company 
    5. Tang, T. L., et al., 'Analytical Design of the CANDU-600 On-line Flux Mapping System,' TDAI-152, Atomic Energy of Canada Limited (1978) 
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    12. G. C. Lee, W. P. Baek, and S. H. Chang, 'Improved Methodology for Generation of Axial Flux Shapes in Digital Core Protection Systems,' Annals of Nuclear Energy, 29, pp. 805-819, (2002) 
    13. R. Jang, 'ANFIS : Adaptive network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., Vol.23, pp.665-685, May-June, 1993 
    14. T. Takagi and M. Sugeno, 'Fuzzy Identification of Systems and Its Applications to Modeling and Control,' IEEETrans.System,Man,Cybern., 1, pp. 116-132, (1985) 
    15. B. O. Cho, et al., MASTER-2.0: Multipurpose Analyzer for Static and Transient Effects of Reactors. KAERI, KAERI/TR-1211/99,(1999) 
  • 이 논문을 인용한 문헌 (1)

    1. 2009. "" Nuclear engineering and technology : an international journal of the Korean Nuclear Society, 41(9): 1181~1190     

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  • 정동원 (2)

  • 신선호 (3)

  • Lee, Ki-Bog (4)

  • Lee, Yoon-Joon (22)

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