Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm

Document Type : Research Paper

Author

Department of Chemical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

Abstract

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0.8 and 5.6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.

Highlights

  • Designing an ANN and optimized it by using a PSO algorithm to predict the Nusselt number of non-Newtonian nanofluids
  • Assessment of the ability of an artificial neural network to predict convective heat transfer experimental data
  • Comparison of ANN-PSO with existing correlation

Keywords


[1] J.C. Maxwell, Electricity and Magnetism, Clarendon Press, Oxford, UK, 1873.
[2] S.U.S. Choi, Enhancing thermal conductivity of fluids with nanoparticles, in: American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FED, 1995, pp. 99-105.
[3] L. Yang, K. Du, A comprehensive review on heat transfer characteristics of TiO2 nanofluids, Int. J. Heat Mass Tran. 108, Part A (2017) 11-31.
[4] K.S. Suganthi, K.S. Rajan, Metal oxide nanofluids: Review of formulation, thermo-physical properties, mechanisms, and heat transfer performance, Renew. Sust. Energ. Rev. 76 (2017) 226-255.
[5] R.M. Sarviya, V. Fuskele, Review on Thermal Conductivity of Nanofluids, Mater. Today-Proc. 4, Part A (2017) 4022-4031.
[6] J.A. Ranga Babu, K.K. Kumar, S. Srinivasa Rao, State-of-art review on hybrid nanofluids, Renew. Sust. Energ. Rev. 77 (2017) 551-565.
[7] K.A. Mohammed, A.R. Abu Talib, A.A. Nuraini and K.A. Ahmed, Review of forced convection nanofluids through corrugated facing step, Renew. Sust. Energ. Rev. 75 (2017) 234-241.
[8] K.Y. Leong, K.Z. Ku Ahmad, H.C. Ong, M.J. Ghazali, A. Baharum, Synthesis and thermal conductivity characteristic of hybrid nanofluids-A review, Renew. Sust. Energ. Rev. 75 (2017) 868-878.
[9] M. Gupta, V. Singh, R. Kumar and Z. Said, A review on thermophysical properties of nanofluids and heat transfer applications, Renew. Sust. Energ. Rev. 74 (2017) 638-670.
[10] R.B. Ganvir, P.V. Walke, V.M. Kriplani, Heat transfer characteristics in nanofluid- A review, Renew. Sust. Energ. Rev. 75 (2017) 451-460.
[11] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network, Int. J. Heat Mass Trans. 54 (2011) 1017-1023.
[12] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Rheological characteristics of non-Newtonian nanofluids: Experimental investigation, Int. Commun. Heat Mass, 38 (2011) 144-148.
[13] M. Hojjat, S.G. Etemad, R. Bagheri, Laminar heat transfer of non-Newtonian nanofluids in a circular tube, Korean J. Chem. Eng., 27 (2010) 1391-1396.
[14] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Laminar convective heat transfer of non-Newtonian nanofluids with constant wall temperature, Heat Mass Transfer, 47 (2011) 203-209.
[15] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Convective heat transfer of non-Newtonian nanofluids through a uniformly heated circular tube, Int. J. Therm. Sci. 50 (2011) 525-531.
[16] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Turbulent forced convection heat transfer of non-Newtonian nanofluids, Exp. Therm Fluid Sci. 35 (2011) 1351-1356.
[17] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Pressure Drop of Non-Newtonian Nanofluids Flowing Through a Horizontal Circular Tube, J. Disper. Sci. Technol. 33 (2012) 1066-1070.
[18] R.V. Pinto, F.A.S. Fiorelli, Review of the mechanisms responsible for heat transfer enhancement using nanofluids, Appl. Therm. Eng. 108 (2016) 720-739.
[19] B. Farajollahi, S.G. Etemad, M. Hojjat, Heat transfer of nanofluids in a shell and tube heat exchanger, Int. J. Heat Mass Trans. 53 (2010) 12-17.
[20] M. Afrand, A. Ahmadi Nadooshan, M. Hassani, H. Yarmand, M. Dahari, Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data, Int. Commun. Heat Mass. 77 (2016) 49-53.
[21] E. Heidari, M.A. Sobati, S. Movahedirad, Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN), Chemometr. Intell. Lab. 155 (2016) 73-85.
[22] A. Alirezaie, S. Saedodin, M.H. Esfe, S.H. Rostamian, Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO-Engine oil hybrid nanofluids and modelling the results with artificial neural networks, J. Mol. Liq. 241 (2017) 173-181.
[23] A.S. Dalkilic, A. Çebi, A. Celen, O. Yıldız, O. Acikgoz, C. Jumpholkul, M. Bayrak, K. Surana, S. Wongwises, Prediction of graphite nanofluids' dynamic viscosity by means of artificial neural networks, Int. Commun. Heat Mass, 73 (2016) 33-42.
[24] M. Hemmat Esfe, M.R. Hassani Ahangar, M. Rejvani, D. Toghraie, M.H. Hajmohammad, Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data, Int. Commun. Heat Mass, 75 (2016) 192-196.
[25] G.A. Longo, C. Zilio, L. Ortombina, M. Zigliotto, Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity, Int. Commun. Heat Mass, 83 (2017) 8-14.
[26] M. Vakili, S. Khosrojerdi, P. Aghajannezhad, M. Yahyaei, A hybrid artificial neural network-genetic algorithm modeling approach for viscosity estimation of graphene nanoplatelets nanofluid using experimental data, Int. Commun. Heat Mass, 82 (2017) 40-48.
[27] M. Hemmat Esfe, S. Saedodin, N. Sina, M. Afrand, S. Rostami, Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid, Int. Commun. Heat Mass, 68 (2015) 50-57.
[28] M. Hemmat Esfe, P. Razi, M.H. Hajmohammad, S.H. Rostamian, W.S. Sarsam, A.A. Abbasian Arani, M. Dahari, Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN, Int. Commun. Heat Mass, 82 (2017) 154-160.
[29] M. Afrand, D. Toghraie, N. Sina, Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: Development of a new correlation and modeled by artificial neural network, Int. Commun. Heat Mass, 75 (2016) 262-269.
[30] E. Ahmadloo, S. Azizi, Prediction of thermal conductivity of various nanofluids using artificial neural network, Int. Commun. Heat Mass, 74 (2016) 69-75.
[31] A. Aminian, Predicting the effective thermal conductivity of nanofluids for intensification of heat transfer using artificial neural network, Powder Technol. 301 (2016) 288-309.
[32] M.A. Ariana, B. Vaferi, G. Karimi, Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks, Powder Technol. 278 (2015) 1-10.
[33] M. Hemmat Esfe, M. Afrand, W-M. Yan and M. Akbari, Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3-water nanofluids using experimental data, Int. Commun. Heat Mass, 66 (2015) 246-249.
[34] M. Hemmat Esfe, S. Wongwises, A. Naderi, A. Asadi, M.R. Safaei, H. Rostamian, M. Dahari, A. Karimipour, Thermal conductivity of Cu/TiO2-water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation, Int. Commun. Heat Mass, 66 (2015) 100-104.
[35] G.A. Longo, C. Zilio, E. Ceseracciu, M. Reggiani, Application of artificial neural network (ANN) for the prediction of thermal conductivity of oxide-water nanofluids, Nano Energy, 1 (2012) 290-296.
[36] M. Vafaei, M. Afrand, N. Sina, R. Kalbasi, F. Sourani, H. Teimouri, Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks, Physica E, 85 (2017) 90-96.
[37] S.H. Rostamian, M. Biglari, S. Saedodin, M. Hemmat Esfe, An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation, J. Mol. Liq. 231 (2017) 364-369.
[38] M. Tahani, M. Vakili, S. Khosrojerdi, Experimental evaluation and ANN modeling of thermal conductivity of graphene oxide nanoplatelets/deionized water nanofluid, Int. Commun. Heat Mass, 76 (2016) 358-365.
[39] A.M. Ghahdarijani, F. Hormozi, A.H. Asl, Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network, Int. Commun. Heat Mass, 84 (2017) 11-19.
[40] B. Vaferi, F. Samimi, E. Pakgohar, D. Mowla, Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes, Powder Technol. 267 (2014) 1-10.
[41] H. Safikhani, A. Abbassi, A. Khalkhali, M. Kalteh, Multi-objective optimization of nanofluid flow in flat tubes using CFD, Artificial Neural Networks and genetic algorithms, Adv. Powder Technol. 25 (2014) 1608-1617.
[42] H. Kalani, M. Sardarabadi, M. Passandideh-Fard, Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector, Appl. Therm. Eng. 113 (2017) 1170-1177.
[43] M. Hojjat, Experimental investigation on convective heat transfer of non-Newtonian nanofluids in circular tube with different thermal boundary conditions, PhD Thesis, Isfahan University of Technology, Iran, 2010.
[44] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Neural Networks, 1995. Proceedings., IEEE International Conference on Neural Networks, IEEE, 1995, pp. 1942-1948.
[45] P. Orbanić, M. Fajdiga, A neural network approach to describing the fretting fatigue in aluminium-steel couplings, Int. J. Fatigue, 25 (2003) 201-207.
[46] R. Eslamloueyan, M.H. Khademi, A neural network-based method for estimation of binary gas diffusivity, Chemometr. Intell. Lab. 104 (2010) 195-204.
[47] C.S. Lee, W. Hwang, H.C. Park, K.S. Han, Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural networks, Compos. Sci. Technol. 59 (1999) 1779-1788.
[48] H.S. Rao, A. Mukherjee, Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites, Comp. Mater. Sci. 5 (1996) 307-322.
[49] I. Kaastra, M. Boyd, Designing a neural network for forecasting financial and economic time series, Neurocomputing, 10 (1996) 215-236.
[50] D.R. Hush, Classification with neural networks: a performance analysis, in: IEEE 1989 International Conference on Systems Engineering, 1989, pp. 277-280.
[51] I. Kanellopoulos, G.G. Wilkinson, Strategies and best practice for neural network image classification, Int. J. Remote Sens. 18 (1997) 711-725.
[52] M.E. Haque, K.V. Sudhakar, ANN back-propagation prediction model for fracture toughness in microalloy steel, Int. J. Fatigue, 24 (2002) 1003-1010.