Using of Artificial Neural Networks (ANNs) to predict the rheological
behavior of MgO-Water nanofluid in a different volume fraction of
nanoparticles, temperatures, and shear rates
Abstract
In this study, the viscosity of MgO-Water nanofluid in a different
volume fraction of nanoparticles, temperatures, and shear rates has been
predicted by Artificial Neural Networks (ANNs) and surface methods. In
the ANN method, an algorithm is proposed to select the best neuron
number for the hidden layer. In the fitting method, a surface is
proposed for each volume fraction of nanoparticles, and finally, the
results of ANN and surface fitting method have been compared. It can be
observed that, increasing the volume fraction from 0.07% to 1.25% at
temperatures of 25, 30, 40, 50, and 60 °C resulted in about two-fold
increase in viscosity. Also, the best network has 24 neurons in the
hidden layer. It can be seen that for a network with 24 neurons in the
hidden layer has the best overall correlation, and this coefficient is
0.999035. The mean absolute value of errors in ANN and fitting method
are 0.0118 and 0.0206, respectively.