The thermal conductivity and viscosity of the water-based hybrid nanofluid containing both Fe3O4 magnetic nanoparticles and carbon nanotubes (CNTs) are measured at the temperatures between 25 and 55 °C, Fe3O4 volume concentrations between 0.1 and 0.9%, and CNT volume concentrations between 0 and 1.35%. To prevent agglomeration and sedimentation of particles, Tetramethylammonium Hydroxide (TMAH) and Gum Arabic (GA) are utilized for coating Fe3O4 nanoparticles and CNTs, respectively. Owing to the interaction between the molecules of these two surfactants, the magnetic nanoparticles and CNTs are connected physically. The variations of the thermal conductivity and viscosity in terms of temperature and both concentrations are evaluated and discussed. A non-Newtonian behavior is observed for this nanofluid, such that its viscosity decreases by the shear rate increment. Using the experimental data, two Artificial Neural Network (ANN) models are developed for prediction of the thermal conductivity and viscosity in terms of effective parameters. The mentioned models have a proper ability to predict these properties.
