TY - JOUR
T1 - Application of an artificial neural network in reactor thermohydraulic problem
T2 - Prediction of critical heat flux
AU - Su, Guanghui
AU - Fukuda, Kenji
AU - Jia, Dounan
AU - Morita, Koji
PY - 2002/1/1
Y1 - 2002/1/1
N2 - 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.
AB - 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.
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U2 - 10.1080/18811248.2002.9715235
DO - 10.1080/18811248.2002.9715235
M3 - Article
AN - SCOPUS:0036577756
SN - 0022-3131
VL - 39
SP - 564
EP - 571
JO - Journal of Nuclear Science and Technology
JF - Journal of Nuclear Science and Technology
IS - 5
ER -