Prediction of Nusselt Number and Friction Factor of Water-Al2O3 Nanofluid Flow in Shell-and-Tube Heat Exchanger with Helical Baffles

被引：27
|
作者：
Bahiraei, Mehdi [1 ]
Hosseinalipour, Seyed Mostafa [2 ,3 ]
Saeedan, Mahdi [2 ,3 ]
机构：
[1] Kermanshah Univ Technol, Sch Energy, Dept Mech Engn, Sadra Ave, Kermanshah 6717863766, Iran
[2] Iran Univ Sci & Technol, Sch Mech Engn, CFD Lab, Tehran, Iran
[3] Iran Univ Sci & Technol, Sch Mech Engn, CAE Ctr, Tehran, Iran
来源：
CHEMICAL ENGINEERING COMMUNICATIONS | 2015年 / 202卷 / 02期
关键词：
Heat exchanger; Heat transfer; Helical baffles; Nanofluid; Neural network; THERMAL-CONDUCTIVITY; TRANSFER ENHANCEMENT; WATER NANOFLUID; SIMULATION; MODEL;
D O I：
10.1080/00986445.2013.840828
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
Heat transfer and flow field of water-Al2O3 nanofluid were simulated three-dimensionally in the shell-side of shell-and-tube heat exchanger with helical baffles. The effects of Reynolds number and volume fraction on heat transfer and pressure drop were evaluated. Increasing the volume fraction and Reynolds number intensified both heat transfer and pressure drop. Reduction of the Reynolds number increased the friction factor, but no considerable change was observed in the friction factor by increasing the volume fraction at constant Reynolds number. Heat transfer of the nanofluid revealed greater dependency on the volume fraction of particles at lower Reynolds numbers. Models of Nusselt number and friction factor were obtained in the heat exchanger in terms of Reynolds number and volume fraction using neural network. The neural network predicted the output variables with great accuracy.
引用
收藏
页码：260 / 268
页数：9
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