Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work

被引:114
作者
Cong, Tenglong [1 ]
Su, Guanghui [1 ]
Qiu, Suizheng [1 ]
Tian, Wenxi [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Nucl Sci & Technol, State Key Lab Multiphase Flow Power Engn, Xian 710049, Peoples R China
关键词
Artificial neural network; Flow regime identification; Pressure drop; Void fraction; Critical heat flux; Heat transfer coefficient; ARTIFICIAL NEURAL-NETWORK; PARTICLE SWARM OPTIMIZATION; LIQUID 2-PHASE FLOW; PRESSURE-DROP; VOID FRACTION; IDENTIFICATION METHOD; SURFACE-ROUGHNESS; TRANSITION REGION; VENTURI SCRUBBERS; VERTICAL TUBES;
D O I
10.1016/j.pnucene.2012.09.003
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Artificial Neural Networks (ANNs) have been applied to deal with flow and heat transfer problems over the past two decades. In the present paper, recent work on the applications of ANNs for predicting the flow regime, pressure drop, void fraction, critical heat flux, onset of nucleate boiling, heat transfer coefficient and boiling curve has been reviewed, respectively. As can be noted in this review work, various types of ANNs can be employed as predictors with acceptable precisions. At the end of this review, methods to improve performance of ANNs and further applications of ANNs in flow and heat transfer problems were introduced. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:54 / 71
页数:18
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