An artificial neural network approach for the prediction of dynamic viscosity of MXene-palm oil nanofluid using experimental data

Abstract

Given the excellent thermal properties of MXene, MXene nanomaterials-based nanofluids may have the potential of being used as heat transfer fluids. In this work, we have investigated the dynamic viscosity of MXene-palm oil nanofluid. To prepare the nanofluid, MXene ( $$\hbox {Ti}_{{3}}\hbox {C}_{{2}}$$ ) nanoflakes were first synthesized by the wet chemistry method. Then, nanofluids with 0.01, 0.03, 0.05, 0.08, 0.1, and 0.2 mass% concentrations of MXene nanoflakes were prepared, and their dynamic viscosity values were determined for a wide temperature range of 18–100 $$^\circ \hbox {C}$$ . The dynamic viscosity of the developed nanofluid, for each concentration, was found to be a strong function of temperature and decreased with increasing temperature. The effect of MXene nanoflakes concentration on the dynamic viscosity was found to be more significant at lower temperatures than at higher temperatures. A multilayer perceptron artificial neural network model was also developed in the study. To develop the model, temperature and nanoflakes concentration were given as inputs to the ANN model, whereas dynamic viscosity was the output of the model. The optimum model was found through the trial and error method. Statistical parameters such as mean square error, mean average percentage error, and correlation coefficient (R) were used to evaluate the performance of the developed model. The values of these parameters, for the optimum ANN model, were found to be 4.733E−05, 0.507%, and 0.99975, respectively. 95.67% of the deviations were also found to be in range of ± 2%. The developed model showed good performance, and its predictions were in excellent agreement with the experimental data.