Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating

被引:107
作者
Hemmat Esfe, Mohammad [1 ]
Rostamian, Hossein [2 ]
Sarlak, Mohammad Reza [1 ]
Rejvani, Mousa [3 ]
Alirezaie, Ali [3 ]
机构
[1] Imam Hossein Univ, Dept Mech Engn, Tehran, Iran
[2] Semnan Univ, Fac Chem Engn, Semnan, Iran
[3] Semnan Univ, Fac Mech Engn, Semnan, Iran
关键词
Nano-oil; rheological behavior; Dynamic viscosity; Neural network; Non-Newtonian fluids; CARBON NANOTUBES/WATER NANOFLUIDS; PREDICT THERMAL-CONDUCTIVITY; SOLID VOLUME FRACTIONS; WATER-BASED NANOFLUIDS; DYNAMIC VISCOSITY; ETHYLENE-GLYCOL; NSGA-II; HEAT-TRANSFER; AQUEOUS NANOFLUID; MODEL DEVELOPMENT;
D O I
10.1016/j.physe.2017.07.012
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In this article, rheological behavior of TiO2-MWCNT (45-55%)/10w40 hybrid nano-oil was studied experimentally. The nano-oils were tested at temperature ranges of 5-55 degrees C and in shear rates up to 11,997 s(-1). With respect to viscosity, shear stress and shear rate variations it was cleared that either of the base oil and nano-oil were non-Newtonian fluids. New equations which were based on thickness of the fluid were presented for different temperature values, R-squared values were between 0.9221 and 0.9998 (the precise of correlation changes depend on temperature). Also to predict the nano-oil behavior, neural network method was utilized. an artificial neural network (MLP type) were used to predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the prediction precise of neural network and correlation the results of these two were compared with together. ANN showed more accurate results in comparison with correlation results. R-2 and (MSE) were 0.9979 and 0.000016 respectively for the ANN.
引用
收藏
页码:231 / 240
页数:10
相关论文
共 58 条
[1]   Fabrication, characterization, and measurement of viscosity of α-Fe2O3-glycerol nanofluids [J].
Abareshi, Maryam ;
Sajjadi, Sayyed Hashem ;
Zebarjad, Seyed Mojtaba ;
Goharshadi, Elaheh K. .
JOURNAL OF MOLECULAR LIQUIDS, 2011, 163 (01) :27-32
[2]  
Alirezaie A., 2017, J MOL LIQ
[3]  
[Anonymous], 2017, THERM ENG+, DOI DOI 10.1016/J.APPLTHERMALENG.2017.02.073
[4]  
[Anonymous], 2017, APPL THERM ENG
[5]  
[Anonymous], 2015, AM J MAT SCI
[6]  
[Anonymous], INT J HEAT MASS TRAN
[7]   An empirical investigation on thermal characteristics and pressure drop of Ag-oil nanofluid in concentric annular tube [J].
Arani, A. A. Abbasian ;
Aberoumand, H. ;
Aberoumand, S. ;
Moghaddam, A. Jafari ;
Dastanian, M. .
HEAT AND MASS TRANSFER, 2016, 52 (08) :1693-1706
[8]   Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system [J].
Atashrouz, Saeid ;
Pazuki, Gholamreza ;
Alimoradi, Younes .
FLUID PHASE EQUILIBRIA, 2014, 372 :43-48
[9]   A New Correlation for Viscosity of Nanofluids with Considering the Temperature Dependence [J].
Bakhshan, Y. ;
Saljooghi, M. .
JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE, 2014, 11 (03) :583-588
[10]   Viscosity of water based SWCNH and TiO2 nanofluids [J].
Bobbo, Sergio ;
Fedele, Laura ;
Benetti, Anna ;
Colla, Laura ;
Fabrizio, Monica ;
Pagura, Cesare ;
Barison, Simona .
EXPERIMENTAL THERMAL AND FLUID SCIENCE, 2012, 36 :65-71