Abstract

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 (varphi=0.25% to 2%) and temperature range of 5 to 55 °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, varphi, 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.

Highlights

  • 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

  • In addition to conducting laboratory studies to identify the properties of ­Al2O3/10W40 nanofluid, the present study examined 400 different ANN structures with different numbers of neurons in each hidden layer, combining different transfer functions in the first and second hidden layers, and separately optimizing the transfer function

  • 134 laboratory data including temperature, shear rate, and φ parameters were used to model the relative viscosity by the ANN

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Summary

Introduction

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. The output layer was taken as an important step in selecting the optimal structure from among the multitude of structures studied to predict the μnf of A­ l2O3 /10W40 nanofluid. In this study, the μnf of ­Al2O3/10W40 nanofluid was modeled by ANN in terms of temperature, φ , and shear rate. In this modeling, ANNs with a large number of neurons and different transfer functions are examined and a suitable and optimal ANN was selected. The following sections provide more complete information on the characteristics of the designed ANN

Objectives
Results
Conclusion

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