The purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant.
Nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity.
The designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations.
This study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils.
To the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature.
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/
