Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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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)
  • Received : 2014.10.08
  • Accepted : 2014.12.05
  • Published : 2015.03.25
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Abstract

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.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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