Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Reactor Vessel Water Level Estimation During Severe Accidents Using Cascaded Fuzzy Neural Networks

  • Kim, Dong Yeong (Department of Nuclear Engineering, Chosun University) ;
  • Yoo, Kwae Hwan (Department of Nuclear Engineering, Chosun University) ;
  • Choi, Geon Pil (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 : 2015.07.23
  • Accepted : 2016.02.01
  • Published : 2016.06.25
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Abstract

Global concern and interest in the safety of nuclear power plants have increased considerably since the Fukushima accident. In the event of a severe accident, the reactor vessel water level cannot be measured. The reactor vessel water level has a direct impact on confirming the safety of reactor core cooling. However, in the event of a severe accident, it may be possible to estimate the reactor vessel water level by employing other information. The cascaded fuzzy neural network (CFNN) model can be used to estimate the reactor vessel water level through the process of repeatedly adding fuzzy neural networks. The developed CFNN model was found to be sufficiently accurate for estimating the reactor vessel water level when the sensor performance had deteriorated. Therefore, the developed CFNN model can help provide effective information to operators in the event of a severe accident.

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References

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