Predicting Diametral Creep of the Pressure Tubes in CANDU Reactors Using Fuzzy Neural Networks
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概要
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Pressure tube (PT) creep is one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of the heat transport system (HTS) in Canada deuterium uranium reactors. PT diametral creep affects the thermal hydraulic characteristics of coolant channels and the critical heat flux (CHF). CHF is a key parameter in determining the critical channel power, which is used in the trip setpoint calculations of regional overpower protection systems. This paper aims to predict PT diametral creep using the measured signals of the HTS by applying fuzzy neural networks (FNNs) according to operating conditions. The FNN model was optimized in terms of its fuzzy rules and parameters by a genetic algorithm combined with the least-squares method. Informative data that demonstrate the system’s characteristic behavior were selected to train the FNN model using the subtractive clustering method. The proposed FNN model for predicting PT diametral creep was verified using the operating data of the Wolsong Unit 1 nuclear power plant in Korea. It was known that the FNN could predict the PT diametral creep accurately. Statistical and analytical uncertainty analysis methods were applied to the models and their uncertainties were evaluated using 60 sampled training and optimization data sets, as well as two fixed test data sets.
- 2011-01-01
著者
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PARK Jong
Department of Internal Medicine, Chonnam National University Hospital
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Na Man
Department Of Nuclear Engineering Chosun University
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Lee Jae
Korea Research Institute Of Chemical Technology
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Lee Jae
Korea Atomic Energy Research Institute
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Park Jong
Department Of Mechanical Engineering Chungnam National University
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Park Jong
Department Mathematics Pusan National University
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Lee Jae
Korea Astronomy And Space Science Institute
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