Effect of magnetic field on Cu-water nanofluid heat transfer using GMDH-type neural network

被引：108

作者：

Sheikholeslami, M. ^{[1
]}

Sheykholeslami, F. Bani ^{[2
]}

Khoshhal, S. ^{[2
]}

Mola-Abasia, H. ^{[3
]}

Ganji, D. D. ^{[1
]}

Rokni, Houman B. ^{[1
,4
]}

机构：

[1] Babol Univ Technol, Dept Mech Engn, Babol Sar, Iran

[2] Babol Univ Technol, Dept Chem Engn, Babol Sar, Iran

[3] Babol Univ Technol, Dept Civil Engn, Babol Sar, Iran

[4] Univ Denver, Denver, CO 80208 USA

来源：

NEURAL COMPUTING & APPLICATIONS | 2014年 / 25卷 / 01期

关键词：

GMDH; Magnetohydrodynamic; Stretching cylinder; Nanofluid; Heat transfer; NATURAL-CONVECTION; FLOW; ENCLOSURE; DECOMPOSITION; PREDICTION; CYLINDER; DESIGN;

D O I：

10.1007/s00521-013-1459-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Heat transfer of Cu-water nanofluid over a stretching cylinder in the presence of magnetic field has been investigated. The group method of data handling (GMDH) type neural networks (NNs) is used to calculate Nusselt number formulation. Results indicate that GMDH-type NN in comparison with fourth-order Runge-Kutta integration scheme provides an effective means of efficiently recognizing the patterns in data and accurately predicting a performance. The effects of nanoparticle volume fraction, magnetic parameter and Reynolds number on Nusselt number are studied by sensitivity analyses. The results show that Nusselt number is an increasing function of Reynolds number and volume fraction of nanoparticles while it is a decreasing function of magnetic parameter. As volume fraction of nanoparticles increases, the effect of this parameter on Nusselt number also increases, but opposite behavior is obtained for magnetic parameter and Reynolds number.

引用

页码：171 / 178

页数：8

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