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. (March 2019)

• Record Type: Journal Article
• Authors: Shahsavar, Amin; Khanmohammadi, Shoaib; Karimipour, Arash; Goodarzi, Marjan
• Is Part Of: International journal of heat and mass transfer. Volume 131(2019)
• Page Start: 432
• Page End: 441
• Publication Date: 2019-03
• Subjects: Experimental study -- Paraffin/Fe3O4 nanofluid -- Thermal conductivity -- Viscosity -- GMDH neural network; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2018.11.069
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: A comprehensive experimental/numerical study of new synthesized nanofluid. Develop correlations for liquid paraffin-Fe3 O4 thermal conductivity & viscosity. Provide a novel statistical approach of "GMDH type of neural network". Effects of temperature and nanoparticles concentration via some experiments. Abstract: This research aims to understand the impacts of volume concentration of Fe3 O4 nanoparticles and temperature on the viscosity & thermal conductivity of liquid paraffin based nanofluid. Several experiments are conducted in the Fe3 O4 concentration range of 0.5–3% and temperature range of 20–90 °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.