Artificial neural network analysis the pulsating Nusselt number and friction factor of TiO2/water nanofluids in the spirally coiled tube with magnetic field
•Heat transfer enhancement techniques are applied to improve the heat exchanger devices which the spirally fluted tube has been used as one of passive heat transfer enhancement techniques to facilitate the desire flow modification for augmenting heat transfer. The spirally fluted tubes are the most...
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| Published in: | International journal of heat and mass transfer Vol. 118; pp. 1152 - 1159 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.03.2018
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| Subjects: | |
| ISSN: | 0017-9310, 1879-2189 |
| Online Access: | Get full text |
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| Abstract | •Heat transfer enhancement techniques are applied to improve the heat exchanger devices which the spirally fluted tube has been used as one of passive heat transfer enhancement techniques to facilitate the desire flow modification for augmenting heat transfer. The spirally fluted tubes are the most widely used tubes in several heat transfer applications. There are many papers presented the thermal devices analysis by using artificial neural network. However, there is not paper concerned the heat transfer characteristics and friction factor of the spirally coiled tube. The objective of this paper is to analyze and predict the pulsating nanofluids heat transfer coefficient and friction factor of the spirally coiled tube with magnetic field effect using artificial neural model. This paper is continuously performed of Naphon et al. [34], which it analyze the Nusselt number and friction factor of nanofluids pulsating flow in the spirally coiled tube with the presence magnetic field effect of 4,6 magnet bars using artificial neural network. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The optimal ANN approach has been applied to show its capability in the representation of thermal performance of the spirally coiled tube with magnetic field.
The application of artificial neural network to analyze the pulsating nanofluids heat transfer and pressure drop in the spirally coiled tube with magnetic field are presented. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The results obtained from the artificial neural network are compared those from the present experiment. It is found that the Levenberg- Marquardt Backpropagation algorithm gives the minimum MSE, and maximum R as compared with other training algorithms. Based on the optimal ANN model, the majority of the data falls within ±2.5%, ±5% of the Nusselt number and friction factor, respectively. The obtained optimal ANN has been applied to predict the thermal performance of the spirally coiled tube with magnetic field. |
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| AbstractList | •Heat transfer enhancement techniques are applied to improve the heat exchanger devices which the spirally fluted tube has been used as one of passive heat transfer enhancement techniques to facilitate the desire flow modification for augmenting heat transfer. The spirally fluted tubes are the most widely used tubes in several heat transfer applications. There are many papers presented the thermal devices analysis by using artificial neural network. However, there is not paper concerned the heat transfer characteristics and friction factor of the spirally coiled tube. The objective of this paper is to analyze and predict the pulsating nanofluids heat transfer coefficient and friction factor of the spirally coiled tube with magnetic field effect using artificial neural model. This paper is continuously performed of Naphon et al. [34], which it analyze the Nusselt number and friction factor of nanofluids pulsating flow in the spirally coiled tube with the presence magnetic field effect of 4,6 magnet bars using artificial neural network. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The optimal ANN approach has been applied to show its capability in the representation of thermal performance of the spirally coiled tube with magnetic field.
The application of artificial neural network to analyze the pulsating nanofluids heat transfer and pressure drop in the spirally coiled tube with magnetic field are presented. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The results obtained from the artificial neural network are compared those from the present experiment. It is found that the Levenberg- Marquardt Backpropagation algorithm gives the minimum MSE, and maximum R as compared with other training algorithms. Based on the optimal ANN model, the majority of the data falls within ±2.5%, ±5% of the Nusselt number and friction factor, respectively. The obtained optimal ANN has been applied to predict the thermal performance of the spirally coiled tube with magnetic field. |
| Author | Arisariyawong, T. Wiriyasart, S. Naphon, P. |
| Author_xml | – sequence: 1 givenname: P. surname: Naphon fullname: Naphon, P. email: paisarnn@g.swu.ac.th – sequence: 2 givenname: S. surname: Wiriyasart fullname: Wiriyasart, S. – sequence: 3 givenname: T. surname: Arisariyawong fullname: Arisariyawong, T. |
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| Cites_doi | 10.1016/j.ijthermalsci.2010.09.006 10.1016/j.jmmm.2017.01.016 10.1016/j.physe.2016.11.035 10.1016/j.applthermaleng.2006.07.036 10.1016/j.nanoen.2011.11.007 10.1016/j.ijheatmasstransfer.2007.03.043 10.1016/j.petrol.2010.02.001 10.1016/j.ijheatmasstransfer.2010.11.039 10.1016/j.icheatmasstransfer.2017.03.014 10.1016/j.ijheatmasstransfer.2017.07.080 10.1016/j.ijthermalsci.2010.11.003 10.1016/j.powtec.2015.04.058 10.1016/j.physe.2016.08.022 10.1016/j.powtec.2015.03.005 10.1016/S0017-9310(99)00369-5 10.1016/j.physe.2017.06.015 10.1016/S1359-4311(02)00155-2 10.1016/j.icheatmasstransfer.2015.08.015 10.1016/j.physe.2016.06.006 10.1016/j.physe.2016.10.013 10.1016/j.fluid.2014.03.031 10.1016/j.expthermflusci.2016.03.026 10.1016/j.expthermflusci.2016.07.011 10.1016/j.icheatmasstransfer.2016.05.013 10.1016/j.ijthermalsci.2008.11.009 10.1016/j.icheatmasstransfer.2016.02.010 10.1016/j.icheatmasstransfer.2006.04.003 10.1016/j.enbuild.2011.03.008 10.1016/j.ijheatmasstransfer.2013.08.013 10.1016/j.icheatmasstransfer.2016.03.008 10.1016/j.physe.2016.11.021 10.1016/j.ijthermalsci.2008.03.012 10.1080/08916159808946559 10.1016/j.icheatmasstransfer.2009.03.009 10.1016/j.ijheatmasstransfer.2006.12.017 10.1016/j.ijheatmasstransfer.2008.10.036 |
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| Keywords | Spirally coiled tube Pulsating nanofluids flow Artificial neural network Magnetic field |
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| References | Xie, Sunden, Wang, Tang (b0080) 2009; 52 Esfe, Afrand, Rostamian, Toghraie (b0020) 2016; 80 Aghanajafi, Toghraie, Mehmandoust (b0050) 2017; 85 Y. Xuan, W. Roetzel, Conceptions of heat transfer correlation of nanofluids, Int. J. Heat Mass Transf. 43 (2000) 3701–3707. Naphon, Wiriyasart (b0170) 2017; 115 Esfe, Afrand, Gharehkhani, Rostamiand, Toghraie, Dahari (b0010) 2016; 76 Santra, Chakraborty, Sen (b0085) 2009; 48 Pak, Cho (b0175) 1998; 11 Zdaniuk, Chamra, Walters (b0065) 2007; 50 Wu, Zhang, Zhang, Zhou, Wang (b0120) 2011; 43 Zhao, Wen, Yang, Li, Wang (b0140) 2015; 281 Toghraie, Mokhtari, Afrand (b0015) 2016; 84 Gao, Sun, Zhou, Shi, Zhao, Wang (b0095) 2009; 48 Alipour, Karimipour, Safaei, Toghraie, Ali (b0030) 2017; 88 Ariana, Vaferi, Karimi (b0155) 2015; 278 Taymaz, Islamoglu (b0090) 2009; 36 Mehrabi, Sharifpur, Meyer (b0135) 2013; 67 Ermis, Erek, Dincer (b0070) 2007; 50 Afrand, Toghraie, Karimipour, Wongwises (b0040) 2017; 430 Ghahdarijani, Hormozi, Asl (b0125) 2017; 84 Haykin (b0200) 1994 Ali, Toghraie, Karimipour, Marzban, Ahmadi (b0025) 2017; 86 Nazari, Toghraie (b0045) 2017; 87 Ahmadloo, Azizi (b0160) 2016; 74 Papari, Yousefi, Moghadasi, Karimi, Campo (b0115) 2011; 50 Kumar, Balaji (b0110) 2011; 50 Islamoglu (b0055) 2003; 23 Longon, Zilio, Ceseracciu, Reggiani (b0130) 2012; 1 Atashrouz, Pazuki, Alimoradi (b0145) 2014; 372 Dalkilic, Çebi, Celen, Yildiz, Acikgoz, Jumpholkul, Bayrak, Surana, Wongwises (b0165) 2016; 73 Shamsi, Ali, Marzban, Toghraie, Mashayekhi (b0035) 2017; 93 Yigit, Ertunc (b0060) 2006; 33 Zarringhalam, Karimipour, Toghraie (b0005) 2016; 76 Esfe, Rostamian, Afrand, Karimipour, Hassani (b0150) 2015; 68 Hojjat, Etemad, Bagheri, Thibault (b0105) 2011; 54 Xie, Wang, Zeng, Luo (b0075) 2007; 27 Drew, Passman (b0185) 1999 Bar, Bandyopadhyay, Biswas, Das (b0100) 2010; 71 Maxwell (b0190) 1881 Coleman, Steele (b0195) 1989 Zarringhalam (10.1016/j.ijheatmasstransfer.2017.11.091_b0005) 2016; 76 Hojjat (10.1016/j.ijheatmasstransfer.2017.11.091_b0105) 2011; 54 Maxwell (10.1016/j.ijheatmasstransfer.2017.11.091_b0190) 1881 Afrand (10.1016/j.ijheatmasstransfer.2017.11.091_b0040) 2017; 430 Taymaz (10.1016/j.ijheatmasstransfer.2017.11.091_b0090) 2009; 36 Bar (10.1016/j.ijheatmasstransfer.2017.11.091_b0100) 2010; 71 Ahmadloo (10.1016/j.ijheatmasstransfer.2017.11.091_b0160) 2016; 74 Esfe (10.1016/j.ijheatmasstransfer.2017.11.091_b0010) 2016; 76 Naphon (10.1016/j.ijheatmasstransfer.2017.11.091_b0170) 2017; 115 Toghraie (10.1016/j.ijheatmasstransfer.2017.11.091_b0015) 2016; 84 Ghahdarijani (10.1016/j.ijheatmasstransfer.2017.11.091_b0125) 2017; 84 Nazari (10.1016/j.ijheatmasstransfer.2017.11.091_b0045) 2017; 87 Alipour (10.1016/j.ijheatmasstransfer.2017.11.091_b0030) 2017; 88 Gao (10.1016/j.ijheatmasstransfer.2017.11.091_b0095) 2009; 48 Dalkilic (10.1016/j.ijheatmasstransfer.2017.11.091_b0165) 2016; 73 Xie (10.1016/j.ijheatmasstransfer.2017.11.091_b0080) 2009; 52 Xie (10.1016/j.ijheatmasstransfer.2017.11.091_b0075) 2007; 27 Atashrouz (10.1016/j.ijheatmasstransfer.2017.11.091_b0145) 2014; 372 Esfe (10.1016/j.ijheatmasstransfer.2017.11.091_b0020) 2016; 80 Pak (10.1016/j.ijheatmasstransfer.2017.11.091_b0175) 1998; 11 Shamsi (10.1016/j.ijheatmasstransfer.2017.11.091_b0035) 2017; 93 Yigit (10.1016/j.ijheatmasstransfer.2017.11.091_b0060) 2006; 33 Zdaniuk (10.1016/j.ijheatmasstransfer.2017.11.091_b0065) 2007; 50 Aghanajafi (10.1016/j.ijheatmasstransfer.2017.11.091_b0050) 2017; 85 Santra (10.1016/j.ijheatmasstransfer.2017.11.091_b0085) 2009; 48 Ariana (10.1016/j.ijheatmasstransfer.2017.11.091_b0155) 2015; 278 Ermis (10.1016/j.ijheatmasstransfer.2017.11.091_b0070) 2007; 50 Kumar (10.1016/j.ijheatmasstransfer.2017.11.091_b0110) 2011; 50 Longon (10.1016/j.ijheatmasstransfer.2017.11.091_b0130) 2012; 1 Haykin (10.1016/j.ijheatmasstransfer.2017.11.091_b0200) 1994 Papari (10.1016/j.ijheatmasstransfer.2017.11.091_b0115) 2011; 50 Islamoglu (10.1016/j.ijheatmasstransfer.2017.11.091_b0055) 2003; 23 Drew (10.1016/j.ijheatmasstransfer.2017.11.091_b0185) 1999 Coleman (10.1016/j.ijheatmasstransfer.2017.11.091_b0195) 1989 Esfe (10.1016/j.ijheatmasstransfer.2017.11.091_b0150) 2015; 68 Ali (10.1016/j.ijheatmasstransfer.2017.11.091_b0025) 2017; 86 Mehrabi (10.1016/j.ijheatmasstransfer.2017.11.091_b0135) 2013; 67 10.1016/j.ijheatmasstransfer.2017.11.091_b0180 Wu (10.1016/j.ijheatmasstransfer.2017.11.091_b0120) 2011; 43 Zhao (10.1016/j.ijheatmasstransfer.2017.11.091_b0140) 2015; 281 |
| References_xml | – volume: 430 start-page: 22 year: 2017 end-page: 28 ident: b0040 article-title: A numerical study of natural convection in a vertical annulus filled with gallium in the presence of magnetic field publication-title: J. Magn. Magn. Mater. – volume: 80 start-page: 384 year: 2016 end-page: 390 ident: b0020 article-title: Examination of rheological behavior of MWCNTs/ZnO-SAE40 hybrid nano-lubricants under various temperatures and solid volume fractions publication-title: Exp. Thermal Fluid Sci. – volume: 54 start-page: 1017 year: 2011 end-page: 1023 ident: b0105 article-title: Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network publication-title: Int. J. Heat Mass Transf. – volume: 67 start-page: 646 year: 2013 end-page: 653 ident: b0135 article-title: Modelling and multi-objective optimization of the convective heat transfer characteristics and pressure drop of low concentration TiO publication-title: Int. J. Heat Mass Transf. – volume: 73 start-page: 33 year: 2016 end-page: 42 ident: b0165 article-title: Prediction of graphite nano fluids’ dynamic viscosity by means of artificial neural networks publication-title: Int. Commun. Heat Mass Transf. – volume: 48 start-page: 1311 year: 2009 end-page: 1318 ident: b0085 article-title: Prediction of heat transfer due to presence of copper–water nanofluid using resilient-propagation neural network publication-title: Int. J. Therm. Sci. – volume: 1 start-page: 290 year: 2012 end-page: 296 ident: b0130 article-title: Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids publication-title: Nano Energy – volume: 88 start-page: 60 year: 2017 end-page: 76 ident: b0030 article-title: Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver-water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel publication-title: Physica E – volume: 71 start-page: 187 year: 2010 end-page: 194 ident: b0100 article-title: Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components publication-title: J. Petrol. Sci. Eng. – year: 1999 ident: b0185 article-title: Theory of Multi Component Fluids – volume: 50 start-page: 3163 year: 2007 end-page: 3175 ident: b0070 article-title: Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network publication-title: Int. J. Heat Mass Transf. – year: 1881 ident: b0190 article-title: A Treatise on electricity and magnetism – volume: 74 start-page: 69 year: 2016 end-page: 75 ident: b0160 article-title: Prediction of thermal conductivity of various nanofluids using artificial neural network publication-title: Int. Commun. Heat Mass Transf. – volume: 23 start-page: 243 year: 2003 end-page: 249 ident: b0055 article-title: A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger use of an artificial neural network model publication-title: Appl. Therm. Eng. – volume: 68 start-page: 98 year: 2015 end-page: 103 ident: b0150 article-title: Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation publication-title: Int. Commun. Heat Mass Transf. – volume: 278 start-page: 1 year: 2015 end-page: 10 ident: b0155 article-title: Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks publication-title: Powder Technol. – volume: 281 start-page: 173 year: 2015 end-page: 183 ident: b0140 article-title: Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks publication-title: Powder Technol. – volume: 76 start-page: 342 year: 2016 end-page: 351 ident: b0005 article-title: Experimental study of the effect of solid volume fraction and Reynolds number on heat transfer coefficient and pressure drop of CuO-water nanofluids publication-title: Exp. Thermal Fluid Sci. – volume: 87 start-page: 134 year: 2017 end-page: 140 ident: b0045 article-title: Numerical simulation of heat transfer and fluid flow of Water-CuO Nanofluid in a sinusoidal channel with a porous medium publication-title: Physica E – volume: 33 start-page: 898 year: 2006 end-page: 907 ident: b0060 article-title: Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks publication-title: Int. Commun. Heat Mass Transf. – volume: 50 start-page: 4713 year: 2007 end-page: 4723 ident: b0065 article-title: Correlating heat transfer and friction in helically-finned tubes using artificial neural networks publication-title: Int. J. Heat Mass Transf. – volume: 50 start-page: 532 year: 2011 end-page: 543 ident: b0110 article-title: ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection publication-title: Int. J. Therm. Sci. – volume: 93 start-page: 167 year: 2017 end-page: 178 ident: b0035 article-title: Increasing heat transfer of non-Newtonian nanofluid in rectangular microchannel with triangular ribs publication-title: Physica E – volume: 50 start-page: 44 year: 2011 end-page: 52 ident: b0115 article-title: Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks publication-title: Int. J. Therm. Sci. – year: 1989 ident: b0195 article-title: Experimental and Uncertainty Analysis for Engineers – volume: 372 start-page: 43 year: 2014 end-page: 48 ident: b0145 article-title: Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system publication-title: Fluid Phase Equilib. – volume: 115 start-page: 537 year: 2017 end-page: 543 ident: b0170 article-title: Pulsating TiO publication-title: Int. J. Heat Mass Transf. – volume: 11 start-page: 151 year: 1998 end-page: 170 ident: b0175 article-title: Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles publication-title: Exper. Heat Transf. – volume: 52 start-page: 2484 year: 2009 end-page: 2497 ident: b0080 article-title: Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks publication-title: Int. J. Heat Mass Transf. – volume: 84 start-page: 152 year: 2016 end-page: 161 ident: b0015 article-title: Molecular dynamic simulation of copper and platinum nanoparticles Poiseuille flow in a nanochannels publication-title: Physica E – volume: 86 start-page: 68 year: 2017 end-page: 75 ident: b0025 article-title: The effect of velocity and dimension of solid nanoparticles on heat transfer in non-Newtonian nanofluids publication-title: Physica E – volume: 85 start-page: 103 year: 2017 end-page: 108 ident: b0050 article-title: Numerical simulation of laminar forced convection of water-CuO nanofluid inside a triangular duct publication-title: Physica E – volume: 43 start-page: 1685 year: 2011 end-page: 1693 ident: b0120 article-title: Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter publication-title: Energy Build. – year: 1994 ident: b0200 article-title: Neural networks – volume: 48 start-page: 583 year: 2009 end-page: 589 ident: b0095 article-title: Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions publication-title: Int. J. Therm. Sci. – volume: 84 start-page: 11 year: 2017 end-page: 19 ident: b0125 article-title: Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network publication-title: Int. Commun. Heat Mass Transf. – volume: 76 start-page: 202 year: 2016 end-page: 208 ident: b0010 article-title: An experimental study on viscosity of alumina-engine oil: effects of temperature and nanoparticles concentration publication-title: Int. Commun. Heat Mass Transfer – volume: 36 start-page: 614 year: 2009 end-page: 617 ident: b0090 article-title: Prediction of convection heat transfer in converging–diverging tube for laminar air flowing using back-propagation neural network publication-title: Int. Commun. Heat Mass Transf. – volume: 27 start-page: 1096 year: 2007 end-page: 1104 ident: b0075 article-title: Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach publication-title: Appl. Therm. Eng. – reference: Y. Xuan, W. Roetzel, Conceptions of heat transfer correlation of nanofluids, Int. J. Heat Mass Transf. 43 (2000) 3701–3707. – volume: 50 start-page: 44 year: 2011 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0115 article-title: Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2010.09.006 – volume: 430 start-page: 22 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0040 article-title: A numerical study of natural convection in a vertical annulus filled with gallium in the presence of magnetic field publication-title: J. Magn. Magn. Mater. doi: 10.1016/j.jmmm.2017.01.016 – year: 1999 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0185 – year: 1994 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0200 – volume: 87 start-page: 134 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0045 article-title: Numerical simulation of heat transfer and fluid flow of Water-CuO Nanofluid in a sinusoidal channel with a porous medium publication-title: Physica E doi: 10.1016/j.physe.2016.11.035 – volume: 27 start-page: 1096 year: 2007 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0075 article-title: Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2006.07.036 – volume: 1 start-page: 290 year: 2012 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0130 article-title: Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids publication-title: Nano Energy doi: 10.1016/j.nanoen.2011.11.007 – volume: 50 start-page: 4713 year: 2007 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0065 article-title: Correlating heat transfer and friction in helically-finned tubes using artificial neural networks publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2007.03.043 – volume: 71 start-page: 187 year: 2010 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0100 article-title: Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2010.02.001 – volume: 54 start-page: 1017 year: 2011 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0105 article-title: Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2010.11.039 – volume: 84 start-page: 11 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0125 article-title: Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2017.03.014 – volume: 115 start-page: 537 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0170 article-title: Pulsating TiO2/water nanofluids flow and heat transfer in the spirally coiled tubes with different magnetic field directions publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2017.07.080 – volume: 50 start-page: 532 year: 2011 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0110 article-title: ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2010.11.003 – volume: 281 start-page: 173 year: 2015 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0140 article-title: Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks publication-title: Powder Technol. doi: 10.1016/j.powtec.2015.04.058 – volume: 85 start-page: 103 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0050 article-title: Numerical simulation of laminar forced convection of water-CuO nanofluid inside a triangular duct publication-title: Physica E doi: 10.1016/j.physe.2016.08.022 – volume: 278 start-page: 1 year: 2015 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0155 article-title: Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks publication-title: Powder Technol. doi: 10.1016/j.powtec.2015.03.005 – ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0180 doi: 10.1016/S0017-9310(99)00369-5 – year: 1989 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0195 – volume: 93 start-page: 167 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0035 article-title: Increasing heat transfer of non-Newtonian nanofluid in rectangular microchannel with triangular ribs publication-title: Physica E doi: 10.1016/j.physe.2017.06.015 – volume: 23 start-page: 243 year: 2003 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0055 article-title: A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger use of an artificial neural network model publication-title: Appl. Therm. Eng. doi: 10.1016/S1359-4311(02)00155-2 – year: 1881 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0190 – volume: 68 start-page: 98 year: 2015 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0150 article-title: Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2015.08.015 – volume: 84 start-page: 152 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0015 article-title: Molecular dynamic simulation of copper and platinum nanoparticles Poiseuille flow in a nanochannels publication-title: Physica E doi: 10.1016/j.physe.2016.06.006 – volume: 86 start-page: 68 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0025 article-title: The effect of velocity and dimension of solid nanoparticles on heat transfer in non-Newtonian nanofluids publication-title: Physica E doi: 10.1016/j.physe.2016.10.013 – volume: 372 start-page: 43 year: 2014 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0145 article-title: Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system publication-title: Fluid Phase Equilib. doi: 10.1016/j.fluid.2014.03.031 – volume: 76 start-page: 342 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0005 article-title: Experimental study of the effect of solid volume fraction and Reynolds number on heat transfer coefficient and pressure drop of CuO-water nanofluids publication-title: Exp. Thermal Fluid Sci. doi: 10.1016/j.expthermflusci.2016.03.026 – volume: 80 start-page: 384 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0020 article-title: Examination of rheological behavior of MWCNTs/ZnO-SAE40 hybrid nano-lubricants under various temperatures and solid volume fractions publication-title: Exp. Thermal Fluid Sci. doi: 10.1016/j.expthermflusci.2016.07.011 – volume: 76 start-page: 202 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0010 article-title: An experimental study on viscosity of alumina-engine oil: effects of temperature and nanoparticles concentration publication-title: Int. Commun. Heat Mass Transfer doi: 10.1016/j.icheatmasstransfer.2016.05.013 – volume: 48 start-page: 1311 year: 2009 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0085 article-title: Prediction of heat transfer due to presence of copper–water nanofluid using resilient-propagation neural network publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2008.11.009 – volume: 73 start-page: 33 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0165 article-title: Prediction of graphite nano fluids’ dynamic viscosity by means of artificial neural networks publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2016.02.010 – volume: 33 start-page: 898 year: 2006 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0060 article-title: Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2006.04.003 – volume: 43 start-page: 1685 year: 2011 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0120 article-title: Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter publication-title: Energy Build. doi: 10.1016/j.enbuild.2011.03.008 – volume: 67 start-page: 646 year: 2013 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0135 article-title: Modelling and multi-objective optimization of the convective heat transfer characteristics and pressure drop of low concentration TiO2/water nanofluids in the turbulent flow regime publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2013.08.013 – volume: 74 start-page: 69 year: 2016 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0160 article-title: Prediction of thermal conductivity of various nanofluids using artificial neural network publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2016.03.008 – volume: 88 start-page: 60 year: 2017 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0030 article-title: Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver-water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel publication-title: Physica E doi: 10.1016/j.physe.2016.11.021 – volume: 48 start-page: 583 year: 2009 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0095 article-title: Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2008.03.012 – volume: 11 start-page: 151 year: 1998 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0175 article-title: Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles publication-title: Exper. Heat Transf. doi: 10.1080/08916159808946559 – volume: 36 start-page: 614 year: 2009 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0090 article-title: Prediction of convection heat transfer in converging–diverging tube for laminar air flowing using back-propagation neural network publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2009.03.009 – volume: 50 start-page: 3163 year: 2007 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0070 article-title: Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2006.12.017 – volume: 52 start-page: 2484 year: 2009 ident: 10.1016/j.ijheatmasstransfer.2017.11.091_b0080 article-title: Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2008.10.036 |
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