Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO - Engine oil hybrid nanofluids and modelling the results with artificial neural networks

Among the various nanofluids, nano-oils are in great attention. What is investigated in this paper is a study on the rheological behavior of oil-based hybrid nanofluids that has been evaluated in the volume fraction, temperature and various shear rates. Experiments have been measured with high accuracy using Cone and Plate viscometer and each experiment was repeated three times to ensure the accuracy of the results. To perform this test, six different volume fractions were considered with percentages of 0.0625, 0.125, 0.25, 0.5, 0.75 and 1 at temperatures of 25, 30, 35, 40, 45 and 50 °C and six shear rates from 670 to 8700 (s− 1). The results show that the dynamic viscosity of nanofluids decreases sharply with increasing temperature. Also nanofluids relatively show non-Newtonian behavior, but this behavior is not very impressive and at a higher temperatures this behavior becomes Newtonian entirely. As a result, increasing in volume fraction of solid, increases the viscosity. A three-variable correlation has been presented to estimate experimental data and experimental results have been modelled by artificial neural networks. The comparison between experimental data and simulation results show high accuracy of modelling by the neural network.