Experimental evaluation and ANN modeling of thermal conductivity of graphene oxide nanoplatelets/deionized water nanofluid ☆

Abstract In this research study, the thermal conductivity of graphene oxide nanoplatelets/deionized water nanofluid is studied in different temperatures and weight fractions using artificial neural network (ANN) and experimental data. For the purpose of training the ANN, the thermal conductivity of nanofluid is measured in temperatures between 25 and 50 °C and weight fractions equal to 0.001, 0.005, 0.015 and 0.045. For the purpose of evaluating the accuracy of the proposed model by ANN, root mean square error (RMSE), R 2 and also mean absolute percentage error (MAPE) are utilized. The best ANN model has two hidden layers and one output layer and also utilizes tansig, logsig and pureline functions and the number of neurons is 4–8–1 in the mentioned layers respectively. The inputs of the ANN model are weight fraction and nanofluid temperature and the output of the network is the thermal conductivity of the nanofluid. The results indicate that the proposed model by ANN can precisely predict the thermal conductivity of the nanofluid.

[1]  Yansheng Yin,et al.  Preparation and thermal conductivity of suspensions of graphite nanoparticles , 2007 .

[2]  S. Wongwises,et al.  Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation☆ , 2015 .

[3]  G. Longo,et al.  Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids , 2012 .

[4]  Gianpiero Colangelo,et al.  Experimental test of an innovative high concentration nanofluid solar collector , 2015 .

[5]  C. Sobhan,et al.  MOLECULAR DYNAMICS MODELING OF THERMAL CONDUCTIVITY ENHANCEMENT IN METAL NANOPARTICLE SUSPENSIONS , 2008 .

[6]  Xianglong Luo,et al.  Investigation on crystallization of TiO2–water nanofluids and deionized water , 2012 .

[7]  B. T. Chew,et al.  Experimental and numerical investigation of thermophysical properties, heat transfer and pressure drop of covalent and noncovalent functionalized graphene nanoplatelet-based water nanofluids in an annular heat exchanger , 2015 .

[8]  Huaqing Xie,et al.  Significant thermal conductivity enhancement for nanofluids containing graphene nanosheets , 2011 .

[9]  Huaqing Xie,et al.  Adjustable thermal conductivity in carbon nanotube nanofluids , 2009 .

[10]  W. Yan,et al.  Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids ☆ , 2015 .

[11]  B. Bruggen,et al.  Thermal Conductivity Prediction of Pure Liquids Using Multi-Layer Perceptron Neural Network , 2015 .

[13]  Enio Pedone Bandarra Filho,et al.  Experimental evaluation of CNT nanofluids in single-phase flow , 2015 .

[14]  S. Ramaprabhu,et al.  Enhanced convective heat transfer using graphene dispersed nanofluids , 2011, Nanoscale research letters.

[15]  R. Mamat,et al.  Experimental Investigation of Thermal Conductivity and Electrical Conductivity of Al2O3 Nanofluid in Water - Ethylene Glycol Mixture for Proton Exchange Membrane Fuel Cell Application , 2015 .

[16]  Zeinab Hajjar,et al.  Enhanced thermal conductivities of graphene oxide nanofluids , 2014 .

[17]  M. Rosen,et al.  Heat transfer and entropy generation for laminar forced convection flow of graphene nanoplatelets nanofluids in a horizontal tube , 2015 .

[18]  S. Wongwises,et al.  Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods , 2015 .

[19]  Wei Yu,et al.  A review on nanofluids: preparation, stability mechanisms, and applications , 2012 .

[20]  M. Vakili,et al.  Thermal conductivity modeling of graphene nanoplatelets/deionized water nanofluid by MLP neural network and theoretical modeling using experimental results , 2016 .

[21]  M. Mehrali,et al.  Investigation of thermal conductivity and rheological properties of nanofluids containing graphene nanoplatelets , 2014, Nanoscale Research Letters.

[22]  Saeed-Reza Sabbagh-Yazdi,et al.  Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution , 2015 .

[23]  Ravikanth S. Vajjha,et al.  Application of nanofluids in heating buildings and reducing pollution , 2009 .

[24]  J. Thibault,et al.  Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network , 2011 .

[25]  Jiahua Zhu,et al.  Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced polytetrafluoroethylene composites with artificial neural networks , 2009 .

[26]  M. Rosen,et al.  Effect of specific surface area on convective heat transfer of graphene nanoplatelet aqueous nanofluids , 2015 .

[27]  M. G. Norton,et al.  X-Ray diffraction : a practical approach , 1998 .

[28]  H. Karimi,et al.  Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks , 2011 .

[29]  S. Delfani,et al.  Experimental investigation of graphene nanoplatelets nanofluid-based volumetric solar collector for domestic hot water systems , 2016 .

[30]  M. Afrand,et al.  Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation , 2015 .

[31]  S. Delfani,et al.  Photothermal properties of graphene nanoplatelets nanofluid for low-temperature direct absorption solar collectors , 2016 .

[32]  M. Afrand,et al.  Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data , 2015 .

[33]  Stephen U. S. Choi.  Enhancing thermal conductivity of fluids with nano-particles , 1995 .

[34]  Hyomin Jeong,et al.  Thermal performance of multi-walled carbon nanotubes (MWCNTs) in aqueous suspensions with surfactants SDBS and SDS ☆ , 2013 .

[35]  R. Saidur,et al.  Stability, thermo-physical properties, and electrical conductivity of graphene oxide-deionized water/ethylene glycol based nanofluid , 2015 .

[36]  Huaqing Xie,et al.  Thermal performance enhancement in nanofluids containing diamond nanoparticles , 2009 .