Application of an Artificial Neural Network in Reactor Thermohydraulic Problem : Prediction of Critical Heat Flux
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概要
- 論文の詳細を見る
A new method for predicting Critical Heat Flux (CHF) with the Artificial Neural Network (ANN) method is presented in this paper. The ANNs were trained based on three conditions: type I (inlet or upstream conditions), II (local or CHF point conditions) and III (outlet or downstream conditions). The best condition for predicting CHF is type II, providing an accuracy of ±10%. The effects of main parameters such as pressure, mass flow rate, equilibrium quality and inlet subcooling on CHF were analyzed using the ANN. Critical heat flux under oscillation flow conditions was also predicted.
- 社団法人 日本原子力学会の論文
- 2002-05-25
著者
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Fukuda K
Department Of Applied Quantum Physics And Nuclear Engineering Kyushu University
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Fukuda Kenji
Department Of Applied Quantum Physics And Nuclear Engineering Kyushu University
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Fukuda K
Kyushu Univ. Fukuoka
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MORITA Koji
National Institute for Materials Science
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MORITA Koji
Kyushu University
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FUKUDA Kenji
Kyushu University
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Jia D
School Of Energy And Power Engineering Xi'an Jiaotong University
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Su Guanghui
School Of Energy And Power Engineering Xi'an Jiaotong University
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Morita Koji
Institute Of Environmental Systems Faculty Of Engineering Kyushu University
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Morita Koji
Department Of Materials Science And Technology Graduate School Of Engineering Sciences Kyushu Univer
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