Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel

被引：242
作者：
Sheikholeslami, M. [1 ,2 ]
Gerdroodbary, M. Barzegar [1 ]
Moradi, R. [3 ]
Shafee, Ahmad [4 ,5 ]
Li, Zhixiong [6 ,7 ]
机构：
[1] Babol Noshirvani Univ Technol, Dept Mech Engn, Babol Sar, Iran
[2] Babol Noshirvani Univ Technol, Renewable Energy Syst & Nanofluid Applicat Heat T, Babol Sar, Iran
[3] Khazar Univ, Sch Engn & Appl Sci, Dept Chem Engn, Baku, Azerbaijan
[4] Univ Tun Hussein Onn Malaysia, FAST, Batu Pahat 86400, Johor State, Malaysia
[5] Coll Technol Studies, Appl Sci Dept, Publ Author Appl Educ & Training, Shuwaikh, Kuwait
[6] Ocean Univ China, Sch Engn, Qingdao 266110, Peoples R China
[7] Univ Wollongong, Sch Mech Mat Mechatron & Biomed Engn, Wollongong, NSW 2522, Australia
来源：
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING | 2019年 / 344卷
关键词：
Nanofluid; Heat transfer; Neural Network; Porous duct; Brownian motion; Expansion ratio; NON-NEWTONIAN NANOFLUID; STAGNATION-POINT FLOW; WALL CARBON NANOTUBES; WATER-BASED NANOFLUID; MAGNETIC-FIELD; TRANSFER INTENSIFICATION; NUMERICAL-SIMULATION; PCM SOLIDIFICATION; VARIABLE VISCOSITY; ENTROPY GENERATION;
D O I：
10.1016/j.cma.2018.09.025
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Heat transfer studying in channels is crucial for transport of the fluids in the oil and gas industry. In this study, numerical approach is applied to analyze the thermal behavior of alumina nanofluid in a duct. Brownian motion impact has been included for predicting nanofluid properties. Neural Network was employed to estimate the heat transfer rate. Numerical data has been obtained via Runge-Kutta method. Our outputs display that GMDH achieved an operative method for an efficient recognition of trends in data. Impact of expansion ratio, nanoparticle concentration, power law index and Reynolds number on Nu was also studied. Our findings reveal that heat transfer intensifies by rise of nanoparticle concentration while it has a reducing trend with rise of expansion ratio. (C) 2018 Elsevier B.V. All rights reserved.
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页码：1 / 12
页数：12
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