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http://dx.doi.org/10.1016/j.net.2016.11.001

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)
Publication Information
Nuclear Engineering and Technology / v.49, no.3, 2017 , pp. 495-503 More about this Journal
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
Artificial Intelligence; Cascaded Fuzzy Neural Networks; Genetic Algorithm; LOCA Break Size; Loss-of-coolant Accident (LOCA);
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Times Cited By KSCI : 5  (Citation Analysis)
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