The effect of graphene nano-powder on the viscosity of water: an experimental study and artificial neural network modeling

Viscosity shifts the flow features of a liquid and affects the consistency of a product, which is a primary factor in demonstrating forces that should be overcome when fluids are transported in pipelines or employed in lubrication. In carbon-based materials, due to their extensive use in industry, finding the simple and reliable equations that can predict the rheological behavior is essential. In this research, the rheological nature of graphene/aqueous nanofluid was examined. Fourier transform infrared spectroscopy, dynamic light scattering, energy-dispersive X-ray spectroscopy, and X-ray powder diffraction were used for analyzing the phase and structure. Transmission electron microscopy and field emission scanning electron microscopy were also employed for micro and nano structural-study. Moreover, nanofluid stability was examined via zeta-potential measurement. Results showed that nanofluid has non-Newtonian nature, the same as the power-law form. Further, from 25 to 50°C, at 12.23 s−1, viscosity decreased by 56.9, 54.9, and 38.5% for 1.0, 2.0, and 3.5 mg/mL nanofluids, respectively. From 25 to 50°C, at 122.3 s−1, viscosity decreased by 42.5, 42.3, and 33.3% for 1.0, 2.0, and 3.5 mg/mL nanofluids, respectively. Besides, to determine the viscosity of nanofluid in varied temperatures and mass concentrations, an artificial neural network via R 2 = 0.999 was applied. Finally, the simple and reliable equations that can predict the rheological behavior of graphene/water nanofluid are calculated