A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: A new approach of GMDH type of neural network

This research aims to understand the impacts of volume concentration of Fe3O4 nanoparticles and temperature on the viscosity & thermal conductivity of liquid paraffin based nanofluid. Several experiments are conducted in the Fe3O4 concentration range of 0.5-3% and temperature range of 20-90 degrees C. Oleic acid is utilized as a surfactant for the improved dispersibility and stability of nanofluids. It was found that the nanofluid behaves as a shear thinning fluid. Additionally, it was revealed that both the thermal conductivity and viscosity boost with increasing the nanoparticle concentration, whereas when the temperature increases the viscosity reduces and the thermal conductivity rises. Moreover, the Artificial Neural Network (ANN) was utilized to model the thermal conductivity and viscosity of the nanofluid using experimental data. The accuracy of the models was assessed based on four known statistical indices including root meant square (RMS), root mean square error (RMSE), mean absolute deviation (MAE), and coefficient of determination (R-2). Results showed that the proposed model of thermal conductivity could estimate outputs with RMS, RMSE, MAE & R-2 values of 0.0678, 0.0179, 0.0041 and 0.96, respectively. (C) 2018 Elsevier Ltd. All rights reserved.