An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of -Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity (mu(nf)) is evaluated at volume fractions (phi=0.25% to 2%) and temperature range of 5 to 55 degrees C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg-Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the mu(nf) of -Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, phi, temperature, and shear rate are considered as input variables, and mu(nf) is considered as output variable. From 400 different ANN structures for -Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E-08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.

Automated summary

This study successfully modeled and predicted the viscosity behavior of -Al2O3/10W40 nanofluid using an artificial neural network (ANN). By selecting the optimal ANN structure, the prediction error was minimized, and the experiment showed that the ANN estimated laboratory data more accurately.