Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)

The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimenta...

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Published inJournal of thermal analysis and calorimetry Vol. 134; no. 3; pp. 2275 - 2286
Main Authors Maddah, Heydar, Aghayari, Reza, Ahmadi, Mohammad Hossein, Rahimzadeh, Mohammad, Ghasemi, Nahid
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.12.2018
Springer Nature B.V
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Abstract The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. Several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. Dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °C. Initially, a self-organizing 6 × 6 hexagonal network was used. A total of 36 neurons were chosen. The winner neuron was neuron 25, having assigned the most data to itself. Then 25 neurons were used for the neural network, which had a very good performance. Temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. Mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. Based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. The mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. Therefore, the model is able to predict relative viscosity with appropriate accuracy.
AbstractList The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. Several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. Dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °C. Initially, a self-organizing 6 × 6 hexagonal network was used. A total of 36 neurons were chosen. The winner neuron was neuron 25, having assigned the most data to itself. Then 25 neurons were used for the neural network, which had a very good performance. Temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. Mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. Based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. The mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. Therefore, the model is able to predict relative viscosity with appropriate accuracy.
The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. Several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. Dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °C. Initially, a self-organizing 6 × 6 hexagonal network was used. A total of 36 neurons were chosen. The winner neuron was neuron 25, having assigned the most data to itself. Then 25 neurons were used for the neural network, which had a very good performance. Temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. Mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. Based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. The mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. Therefore, the model is able to predict relative viscosity with appropriate accuracy.
Author Ghasemi, Nahid
Rahimzadeh, Mohammad
Ahmadi, Mohammad Hossein
Maddah, Heydar
Aghayari, Reza
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Cites_doi 10.1007/s00231-016-1906-2
10.1007/s00231-016-1941-z
10.1090/qam/10666
10.1007/s10973-016-6002-9
10.1016/j.aej.2014.09.007
10.1016/J.APPLTHERMALENG.2017.06.077
10.1061/(ASCE)0887-3801(1997)11:1(74)
10.1016/j.colsurfa.2009.11.044
10.22038/NMJ.2018.005.0001
10.1063/1.1861145
10.1016/j.molliq.2018.05.124
10.1016/J.APPLTHERMALENG.2017.05.200
10.1007/s00231-017-2068-6
10.1260/0144-5987.33.5.659
10.1016/j.neunet.2012.09.018
10.2174/1573413713666161213114458
10.1007/s00231-018-2292-8
10.1016/j.ijthermalsci.2012.10.016
10.1007/s10973-016-5469-8
10.1115/imece2016-66039
10.1016/j.rser.2018.04.042
10.1016/j.icheatmasstransfer.2017.12.006
10.1016/j.applthermaleng.2017.01.068
10.1134/S1063785013090125
10.1016/J.EXPTHERMFLUSCI.2015.11.018
10.1007/s11242-014-0375-7
10.1007/s10973-018-7035-z
10.1007/s10973-014-3771-x
10.1080/01457632.2013.810086
10.1016/j.ijheatmasstransfer.2017.09.070
10.1007/s00231-017-2261-7
10.1016/J.MOLLIQ.2017.05.121
10.1016/j.colsurfa.2014.07.017
10.1016/j.icheatmasstransfer.2015.06.013
10.1016/J.COLSURFA.2018.01.030
10.1016/J.COLSURFA.2017.06.084
10.1007/s10973-017-6680-y
10.1155/2014/274560
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PublicationSubtitle An International Forum for Thermal Studies
PublicationTitle Journal of thermal analysis and calorimetry
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References Hemmat Esfe, Esfandeh, Saedodin, Rostamian (CR27) 2017; 125
Mukherjee (CR36) 1997; 11
Alhuyi Nazari, Ahmadi, Ghasempour, Shafii (CR4) 2018; 91
Pal, Mallick, Pal (CR20) 2014; 459
Hemmat Esfe, Saedodin, Sina, Afrand, Rostami (CR39) 2015; 68
Ahmadi, Ahmadi, Nazari, Mahian, Ghasempour (CR6) 2018
Gandomkar, Saidi, Shafii, Vandadi, Kalan (CR16) 2017; 116
Ahmadi, Alhuyi Nazari, Ghasempour, Madah, Shafii, Ahmadi (CR10) 2018; 541
Sheremet, Shenoy, Pop (CR28) 2016
Ghasemi, Aghayari, Maddah (CR8) 2018
Taslimifar, Mohammadi, Afshin, Saidi, Shafii (CR12) 2013; 65
Hemmat Esfe, Ahangar, Toghraie, Hajmohammad, Rostamian, Tourang (CR25) 2016; 126
Rahman, Leong, Idris, Saad, Anwar (CR1) 2017; 53
Amin, Roghayeh, Fatemeh, Fatollah (CR14) 2015; 33
Kohonen (CR32) 2013; 37
Maddah, Ghasemi (CR9) 2017; 53
Mohammadi, Mohammadi, Ghahremani, Shafii, Mohammadi (CR13) 2014; 35
Levenberg (CR35) 1944; 2
Maddah, Ghasemi, Keyvani, Cheraghali (CR31) 2017; 53
Alias, Johari, Ngadi, Zaine (CR34) 2015; 9
Lenin, Joy (CR21) 2017; 529
Jamshidi, Farhadi, Ganji, Sedighi (CR22) 2012; 25
Ahmadi, Mirlohi, Nazari, Ghasempour (CR5) 2018
Rudyak, Dimov, Kuznetsov (CR38) 2013; 39
Esfe, Rejvani, Karimpour, Abbasian Arani (CR24) 2017; 128
Nazari, Ghasempour, Ahmadi, Heydarian, Shafii (CR15) 2018; 91
Sheremet, Pop (CR29) 2014; 105
Akbarianrad, Mohammadian, Alhuyi Nazari, Rahbani Nobar (CR3) 2018; 5
Hemmat Esfe, Wongwises, Rejvani (CR23) 2017; 13
Barzegarian, Moraveji, Aloueyan (CR11) 2016; 74
Yu, Xie, Chen, Li (CR19) 2010; 355
Sheremet, Mahian, Pop (CR30) 2018; 116
CR7
Hemmat Esfe, Esfandeh, Rejvani (CR26) 2017
Alirezaie, Saedodin, Esfe, Rostamian (CR40) 2017; 241
Hong, Yang, Choi (CR17) 2005; 97
Moghaddam, Motahari (CR41) 2017; 123
Esfe, Saedodin, Mahian, Wongwises (CR18) 2014; 117
Ghasemi, Aghayari, Maddah (CR33) 2017
Chaudhary, Bhatia, Ahlawat (CR37) 2014; 53
Negm, Abdel-Rehim, Attia (CR2) 2016; 8
MH Ahmadi (7827_CR5) 2018
M Hemmat Esfe (7827_CR39) 2015; 68
MA Moghaddam (7827_CR41) 2017; 123
7827_CR7
K Levenberg (7827_CR35) 1944; 2
M Sheremet (7827_CR28) 2016
MNA Negm (7827_CR2) 2016; 8
H Maddah (7827_CR9) 2017; 53
MRA Rahman (7827_CR1) 2017; 53
M Sheremet (7827_CR29) 2014; 105
T Kohonen (7827_CR32) 2013; 37
A Gandomkar (7827_CR16) 2017; 116
MH Esfe (7827_CR18) 2014; 117
A Alirezaie (7827_CR40) 2017; 241
MH Ahmadi (7827_CR6) 2018
VY Rudyak (7827_CR38) 2013; 39
M Hemmat Esfe (7827_CR25) 2016; 126
A Mukherjee (7827_CR36) 1997; 11
MH Ahmadi (7827_CR10) 2018; 541
TE Amin (7827_CR14) 2015; 33
H Maddah (7827_CR31) 2017; 53
R Barzegarian (7827_CR11) 2016; 74
B Pal (7827_CR20) 2014; 459
H Alias (7827_CR34) 2015; 9
N Akbarianrad (7827_CR3) 2018; 5
M Hemmat Esfe (7827_CR27) 2017; 125
N Ghasemi (7827_CR33) 2017
M Mohammadi (7827_CR13) 2014; 35
MA Nazari (7827_CR15) 2018; 91
W Yu (7827_CR19) 2010; 355
V Chaudhary (7827_CR37) 2014; 53
M Hemmat Esfe (7827_CR23) 2017; 13
MH Esfe (7827_CR24) 2017; 128
N Jamshidi (7827_CR22) 2012; 25
M Hemmat Esfe (7827_CR26) 2017
M Alhuyi Nazari (7827_CR4) 2018; 91
M Taslimifar (7827_CR12) 2013; 65
R Lenin (7827_CR21) 2017; 529
N Ghasemi (7827_CR8) 2018
M Sheremet (7827_CR30) 2018; 116
T-K Hong (7827_CR17) 2005; 97
References_xml – volume: 53
  start-page: 1413
  year: 2017
  end-page: 1423
  ident: CR31
  article-title: Experimental and numerical study of nanofluid in heat exchanger fitted by modified twisted tape: exergy analysis and ANN prediction model
  publication-title: Heat Mass Transf Und Stoffuebertragung
  doi: 10.1007/s00231-016-1906-2
  contributor:
    fullname: Cheraghali
– volume: 53
  start-page: 1835
  year: 2017
  end-page: 1842
  ident: CR1
  article-title: Numerical analysis of the forced convective heat transfer on Al O –Cu/water hybrid nanofluid
  publication-title: Heat Mass Transf Und Stoffuebertragung
  doi: 10.1007/s00231-016-1941-z
  contributor:
    fullname: Anwar
– volume: 2
  start-page: 164
  year: 1944
  end-page: 168
  ident: CR35
  article-title: A method for the solution of certain non-linear problems in least squares
  publication-title: Q Appl Math
  doi: 10.1090/qam/10666
  contributor:
    fullname: Levenberg
– volume: 128
  start-page: 1359
  year: 2017
  end-page: 1371
  ident: CR24
  article-title: Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-016-6002-9
  contributor:
    fullname: Abbasian Arani
– volume: 53
  start-page: 827
  year: 2014
  end-page: 831
  ident: CR37
  article-title: A novel self-organizing map (SOM) learning algorithm with nearest and farthest neurons
  publication-title: Alex Eng J
  doi: 10.1016/j.aej.2014.09.007
  contributor:
    fullname: Ahlawat
– volume: 125
  start-page: 673
  year: 2017
  end-page: 685
  ident: CR27
  article-title: Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications
  publication-title: Appl Therm Eng
  doi: 10.1016/J.APPLTHERMALENG.2017.06.077
  contributor:
    fullname: Rostamian
– volume: 11
  start-page: 74
  year: 1997
  end-page: 77
  ident: CR36
  article-title: Self-organizing neural network for identification of natural modes
  publication-title: J Comput Civil Eng
  doi: 10.1061/(ASCE)0887-3801(1997)11:1(74)
  contributor:
    fullname: Mukherjee
– volume: 355
  start-page: 109
  year: 2010
  end-page: 113
  ident: CR19
  article-title: Enhancement of thermal conductivity of kerosene-based Fe O nanofluids prepared via phase-transfer method
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/j.colsurfa.2009.11.044
  contributor:
    fullname: Li
– volume: 5
  start-page: 121
  year: 2018
  end-page: 126
  ident: CR3
  article-title: Applications of nanotechnology in endodontic: a review
  publication-title: Nanomed J
  doi: 10.22038/NMJ.2018.005.0001
  contributor:
    fullname: Rahbani Nobar
– volume: 97
  start-page: 064311
  year: 2005
  ident: CR17
  article-title: Study of the enhanced thermal conductivity of Fe nanofluids
  publication-title: J Appl Phys
  doi: 10.1063/1.1861145
  contributor:
    fullname: Choi
– year: 2018
  ident: CR5
  article-title: A review of thermal conductivity of various nanofluids
  publication-title: J Mol Liq
  doi: 10.1016/j.molliq.2018.05.124
  contributor:
    fullname: Ghasempour
– volume: 123
  start-page: 1419
  year: 2017
  end-page: 1433
  ident: CR41
  article-title: Experimental investigation, sensitivity analysis and modeling of rheological behavior of MWCNT-CuO (30–70)/SAE40 hybrid nano-lubricant
  publication-title: Appl Therm Eng
  doi: 10.1016/J.APPLTHERMALENG.2017.05.200
  contributor:
    fullname: Motahari
– volume: 53
  start-page: 3459
  year: 2017
  end-page: 3472
  ident: CR9
  article-title: Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-017-2068-6
  contributor:
    fullname: Ghasemi
– volume: 33
  start-page: 659
  year: 2015
  end-page: 676
  ident: CR14
  article-title: Evaluation of nanoparticle shape effect on a nanofluid based flat-plate solar collector efficiency
  publication-title: Energy Explor Exploit
  doi: 10.1260/0144-5987.33.5.659
  contributor:
    fullname: Fatollah
– volume: 37
  start-page: 52
  year: 2013
  end-page: 65
  ident: CR32
  article-title: Essentials of the self-organizing map
  publication-title: Neural Netw
  doi: 10.1016/j.neunet.2012.09.018
  contributor:
    fullname: Kohonen
– volume: 13
  start-page: 324
  year: 2017
  end-page: 329
  ident: CR23
  article-title: Prediction of thermal conductivity of carbon nanotube-EG nanofluid using experimental data by ANN
  publication-title: Curr Nanosci
  doi: 10.2174/1573413713666161213114458
  contributor:
    fullname: Rejvani
– year: 2018
  ident: CR8
  article-title: Optimizing the parameters of heat transmission in a small heat exchanger with spiral tapes cut as triangles and aluminum oxide nanofluid using central composite design method
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-018-2292-8
  contributor:
    fullname: Maddah
– volume: 65
  start-page: 234
  year: 2013
  end-page: 241
  ident: CR12
  article-title: Overall thermal performance of ferrofluidic open loop pulsating heat pipes: an experimental approach
  publication-title: Int J Therm Sci
  doi: 10.1016/j.ijthermalsci.2012.10.016
  contributor:
    fullname: Shafii
– volume: 126
  start-page: 837
  year: 2016
  end-page: 843
  ident: CR25
  article-title: Designing artificial neural network on thermal conductivity of Al O –water–EG (60–40%) nanofluid using experimental data
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-016-5469-8
  contributor:
    fullname: Tourang
– volume: 8
  start-page: V008T10A097
  year: 2016
  ident: CR2
  article-title: Investigating the effect of Al O /water nanofluid on the efficiency of a thermosyphon flat-plate solar collector
  publication-title: Heat Transf Therm Eng ASME
  doi: 10.1115/imece2016-66039
  contributor:
    fullname: Attia
– year: 2016
  ident: CR28
  publication-title: Convective flow and heat transfer from wavy surfaces: viscous fluids, porous media and nanofluids
  contributor:
    fullname: Pop
– volume: 91
  start-page: 630
  year: 2018
  end-page: 638
  ident: CR4
  article-title: How to improve the thermal performance of pulsating heat pipes: a review on working fluid
  publication-title: Renew Sustain Energy Rev
  doi: 10.1016/j.rser.2018.04.042
  contributor:
    fullname: Shafii
– volume: 91
  start-page: 90
  year: 2018
  end-page: 94
  ident: CR15
  article-title: Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe
  publication-title: Int Commun Heat Mass Transf
  doi: 10.1016/j.icheatmasstransfer.2017.12.006
  contributor:
    fullname: Shafii
– volume: 116
  start-page: 56
  year: 2017
  end-page: 65
  ident: CR16
  article-title: Visualization and comparative investigations of pulsating ferro-fluid heat pipe
  publication-title: Appl Therm Eng
  doi: 10.1016/j.applthermaleng.2017.01.068
  contributor:
    fullname: Kalan
– volume: 39
  start-page: 779
  year: 2013
  end-page: 782
  ident: CR38
  article-title: On the dependence of the viscosity coefficient of nanofluids on particle size and temperature
  publication-title: Tech Phys Lett
  doi: 10.1134/S1063785013090125
  contributor:
    fullname: Kuznetsov
– volume: 74
  start-page: 11
  year: 2016
  end-page: 18
  ident: CR11
  article-title: Experimental investigation on heat transfer characteristics and pressure drop of BPHE (brazed plate heat exchanger) using TiO –water nanofluid
  publication-title: Exp Therm Fluid Sci
  doi: 10.1016/J.EXPTHERMFLUSCI.2015.11.018
  contributor:
    fullname: Aloueyan
– volume: 105
  start-page: 411
  year: 2014
  end-page: 429
  ident: CR29
  article-title: Natural convection in a square porous cavity with sinusoidal temperature distributions on both side walls filled with a nanofluid: Buongiorno’s mathematical model
  publication-title: Transp Porous Media
  doi: 10.1007/s11242-014-0375-7
  contributor:
    fullname: Pop
– year: 2018
  ident: CR6
  article-title: A proposed model to predict thermal conductivity ratio of Al O /EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-018-7035-z
  contributor:
    fullname: Ghasempour
– volume: 117
  start-page: 675
  year: 2014
  end-page: 681
  ident: CR18
  article-title: Thermal conductivity of Al O /water nanofluids: measurement, correlation, sensitivity analysis, and comparisons with literature reports
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-014-3771-x
  contributor:
    fullname: Wongwises
– volume: 35
  start-page: 25
  year: 2014
  end-page: 33
  ident: CR13
  article-title: Experimental investigation of thermal resistance of a ferrofluidic closed-loop pulsating heat pipe
  publication-title: Heat Transf Eng
  doi: 10.1080/01457632.2013.810086
  contributor:
    fullname: Mohammadi
– volume: 9
  start-page: 43
  year: 2015
  end-page: 48
  ident: CR34
  article-title: Thermal and flow behaviour of Titania-deionized water nanofluids
  publication-title: Adv Environ Biol
  contributor:
    fullname: Zaine
– volume: 116
  start-page: 751
  year: 2018
  end-page: 761
  ident: CR30
  article-title: Natural convection in an inclined cavity with time-periodic temperature boundary conditions using nanofluids: application in solar collectors
  publication-title: Int J Heat Mass Transf
  doi: 10.1016/j.ijheatmasstransfer.2017.09.070
  contributor:
    fullname: Pop
– year: 2017
  ident: CR33
  article-title: Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-017-2261-7
  contributor:
    fullname: Maddah
– volume: 241
  start-page: 173
  year: 2017
  end-page: 181
  ident: CR40
  article-title: Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO—engine oil hybrid nanofluids and modelling the results with artificial neural networks
  publication-title: J Mol Liq
  doi: 10.1016/J.MOLLIQ.2017.05.121
  contributor:
    fullname: Rostamian
– volume: 459
  start-page: 282
  year: 2014
  end-page: 289
  ident: CR20
  article-title: Anisotropic CuO nanostructures of different size and shape exhibit thermal conductivity superior than typical bulk powder
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/j.colsurfa.2014.07.017
  contributor:
    fullname: Pal
– ident: CR7
– volume: 68
  start-page: 50
  year: 2015
  end-page: 57
  ident: CR39
  article-title: Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid
  publication-title: Int Commun Heat Mass Transf
  doi: 10.1016/j.icheatmasstransfer.2015.06.013
  contributor:
    fullname: Rostami
– volume: 541
  start-page: 154
  year: 2018
  end-page: 164
  ident: CR10
  article-title: Thermal conductivity ratio prediction of Al O /water nanofluid by applying connectionist methods
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/J.COLSURFA.2018.01.030
  contributor:
    fullname: Ahmadi
– volume: 529
  start-page: 922
  year: 2017
  end-page: 929
  ident: CR21
  article-title: Role of base fluid on the thermal conductivity of oleic acid coated magnetite nanofluids
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/J.COLSURFA.2017.06.084
  contributor:
    fullname: Joy
– volume: 25
  start-page: 201
  year: 2012
  end-page: 209
  ident: CR22
  article-title: Experimental investigation on the viscosity of nanofluids
  publication-title: Int J Eng Trans B Appl
  contributor:
    fullname: Sedighi
– year: 2017
  ident: CR26
  article-title: Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-017-6680-y
  contributor:
    fullname: Rejvani
– year: 2018
  ident: 7827_CR8
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-018-2292-8
  contributor:
    fullname: N Ghasemi
– volume: 33
  start-page: 659
  year: 2015
  ident: 7827_CR14
  publication-title: Energy Explor Exploit
  doi: 10.1260/0144-5987.33.5.659
  contributor:
    fullname: TE Amin
– volume: 126
  start-page: 837
  year: 2016
  ident: 7827_CR25
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-016-5469-8
  contributor:
    fullname: M Hemmat Esfe
– volume: 241
  start-page: 173
  year: 2017
  ident: 7827_CR40
  publication-title: J Mol Liq
  doi: 10.1016/J.MOLLIQ.2017.05.121
  contributor:
    fullname: A Alirezaie
– volume: 529
  start-page: 922
  year: 2017
  ident: 7827_CR21
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/J.COLSURFA.2017.06.084
  contributor:
    fullname: R Lenin
– volume: 65
  start-page: 234
  year: 2013
  ident: 7827_CR12
  publication-title: Int J Therm Sci
  doi: 10.1016/j.ijthermalsci.2012.10.016
  contributor:
    fullname: M Taslimifar
– ident: 7827_CR7
  doi: 10.1155/2014/274560
– volume: 125
  start-page: 673
  year: 2017
  ident: 7827_CR27
  publication-title: Appl Therm Eng
  doi: 10.1016/J.APPLTHERMALENG.2017.06.077
  contributor:
    fullname: M Hemmat Esfe
– volume-title: Convective flow and heat transfer from wavy surfaces: viscous fluids, porous media and nanofluids
  year: 2016
  ident: 7827_CR28
  contributor:
    fullname: M Sheremet
– year: 2018
  ident: 7827_CR6
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-018-7035-z
  contributor:
    fullname: MH Ahmadi
– year: 2018
  ident: 7827_CR5
  publication-title: J Mol Liq
  doi: 10.1016/j.molliq.2018.05.124
  contributor:
    fullname: MH Ahmadi
– volume: 8
  start-page: V008T10A097
  year: 2016
  ident: 7827_CR2
  publication-title: Heat Transf Therm Eng ASME
  doi: 10.1115/imece2016-66039
  contributor:
    fullname: MNA Negm
– volume: 25
  start-page: 201
  year: 2012
  ident: 7827_CR22
  publication-title: Int J Eng Trans B Appl
  contributor:
    fullname: N Jamshidi
– volume: 105
  start-page: 411
  year: 2014
  ident: 7827_CR29
  publication-title: Transp Porous Media
  doi: 10.1007/s11242-014-0375-7
  contributor:
    fullname: M Sheremet
– volume: 116
  start-page: 751
  year: 2018
  ident: 7827_CR30
  publication-title: Int J Heat Mass Transf
  doi: 10.1016/j.ijheatmasstransfer.2017.09.070
  contributor:
    fullname: M Sheremet
– volume: 39
  start-page: 779
  year: 2013
  ident: 7827_CR38
  publication-title: Tech Phys Lett
  doi: 10.1134/S1063785013090125
  contributor:
    fullname: VY Rudyak
– volume: 91
  start-page: 630
  year: 2018
  ident: 7827_CR4
  publication-title: Renew Sustain Energy Rev
  doi: 10.1016/j.rser.2018.04.042
  contributor:
    fullname: M Alhuyi Nazari
– volume: 91
  start-page: 90
  year: 2018
  ident: 7827_CR15
  publication-title: Int Commun Heat Mass Transf
  doi: 10.1016/j.icheatmasstransfer.2017.12.006
  contributor:
    fullname: MA Nazari
– volume: 53
  start-page: 827
  year: 2014
  ident: 7827_CR37
  publication-title: Alex Eng J
  doi: 10.1016/j.aej.2014.09.007
  contributor:
    fullname: V Chaudhary
– volume: 37
  start-page: 52
  year: 2013
  ident: 7827_CR32
  publication-title: Neural Netw
  doi: 10.1016/j.neunet.2012.09.018
  contributor:
    fullname: T Kohonen
– volume: 5
  start-page: 121
  year: 2018
  ident: 7827_CR3
  publication-title: Nanomed J
  doi: 10.22038/NMJ.2018.005.0001
  contributor:
    fullname: N Akbarianrad
– year: 2017
  ident: 7827_CR33
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-017-2261-7
  contributor:
    fullname: N Ghasemi
– volume: 53
  start-page: 1835
  year: 2017
  ident: 7827_CR1
  publication-title: Heat Mass Transf Und Stoffuebertragung
  doi: 10.1007/s00231-016-1941-z
  contributor:
    fullname: MRA Rahman
– volume: 459
  start-page: 282
  year: 2014
  ident: 7827_CR20
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/j.colsurfa.2014.07.017
  contributor:
    fullname: B Pal
– volume: 9
  start-page: 43
  year: 2015
  ident: 7827_CR34
  publication-title: Adv Environ Biol
  contributor:
    fullname: H Alias
– volume: 74
  start-page: 11
  year: 2016
  ident: 7827_CR11
  publication-title: Exp Therm Fluid Sci
  doi: 10.1016/J.EXPTHERMFLUSCI.2015.11.018
  contributor:
    fullname: R Barzegarian
– volume: 68
  start-page: 50
  year: 2015
  ident: 7827_CR39
  publication-title: Int Commun Heat Mass Transf
  doi: 10.1016/j.icheatmasstransfer.2015.06.013
  contributor:
    fullname: M Hemmat Esfe
– volume: 541
  start-page: 154
  year: 2018
  ident: 7827_CR10
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/J.COLSURFA.2018.01.030
  contributor:
    fullname: MH Ahmadi
– volume: 117
  start-page: 675
  year: 2014
  ident: 7827_CR18
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-014-3771-x
  contributor:
    fullname: MH Esfe
– volume: 53
  start-page: 1413
  year: 2017
  ident: 7827_CR31
  publication-title: Heat Mass Transf Und Stoffuebertragung
  doi: 10.1007/s00231-016-1906-2
  contributor:
    fullname: H Maddah
– volume: 123
  start-page: 1419
  year: 2017
  ident: 7827_CR41
  publication-title: Appl Therm Eng
  doi: 10.1016/J.APPLTHERMALENG.2017.05.200
  contributor:
    fullname: MA Moghaddam
– volume: 97
  start-page: 064311
  year: 2005
  ident: 7827_CR17
  publication-title: J Appl Phys
  doi: 10.1063/1.1861145
  contributor:
    fullname: T-K Hong
– volume: 355
  start-page: 109
  year: 2010
  ident: 7827_CR19
  publication-title: Colloids Surf A Physicochem Eng Asp
  doi: 10.1016/j.colsurfa.2009.11.044
  contributor:
    fullname: W Yu
– year: 2017
  ident: 7827_CR26
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-017-6680-y
  contributor:
    fullname: M Hemmat Esfe
– volume: 53
  start-page: 3459
  year: 2017
  ident: 7827_CR9
  publication-title: Heat Mass Transf
  doi: 10.1007/s00231-017-2068-6
  contributor:
    fullname: H Maddah
– volume: 35
  start-page: 25
  year: 2014
  ident: 7827_CR13
  publication-title: Heat Transf Eng
  doi: 10.1080/01457632.2013.810086
  contributor:
    fullname: M Mohammadi
– volume: 116
  start-page: 56
  year: 2017
  ident: 7827_CR16
  publication-title: Appl Therm Eng
  doi: 10.1016/j.applthermaleng.2017.01.068
  contributor:
    fullname: A Gandomkar
– volume: 128
  start-page: 1359
  year: 2017
  ident: 7827_CR24
  publication-title: J Therm Anal Calorim
  doi: 10.1007/s10973-016-6002-9
  contributor:
    fullname: MH Esfe
– volume: 13
  start-page: 324
  year: 2017
  ident: 7827_CR23
  publication-title: Curr Nanosci
  doi: 10.2174/1573413713666161213114458
  contributor:
    fullname: M Hemmat Esfe
– volume: 2
  start-page: 164
  year: 1944
  ident: 7827_CR35
  publication-title: Q Appl Math
  doi: 10.1090/qam/10666
  contributor:
    fullname: K Levenberg
– volume: 11
  start-page: 74
  year: 1997
  ident: 7827_CR36
  publication-title: J Comput Civil Eng
  doi: 10.1061/(ASCE)0887-3801(1997)11:1(74)
  contributor:
    fullname: A Mukherjee
SSID ssj0009901
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Snippet The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers)...
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springer
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SubjectTerms Analytical Chemistry
Artificial neural networks
Automotive engineering
Carbon
Chemistry
Chemistry and Materials Science
Correlation coefficients
Inorganic Chemistry
Mathematical models
Measurement Science and Instrumentation
Multi wall carbon nanotubes
Nanofluids
Nanoparticles
Neural networks
Neurons
Physical Chemistry
Polymer Sciences
Predictions
Self organizing maps
Standard deviation
Viscosity
Title Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)
URI https://link.springer.com/article/10.1007/s10973-018-7827-1
https://www.proquest.com/docview/2139654093
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