Abstract
In this study, the influence of different volume fractions (phi) of nanoparticles and temperatures on the dynamic viscosity (mu_{nf}) of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the mu_{nf} was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different phi, 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and phi) and one output (mu_{nf}) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting mu_{nf}. The results show that an increase phi has a significant effect on mu_{nf} value. As phi increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the mu_{nf}.
Highlights
In this study, the influence of different volume fractions ( φ ) of nanoparticles and temperatures on the dynamic viscosity of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid was examined by artificial neural network (ANN)
One or more solid particles are added to the fluid, which increases the rate of heat transfer and change in viscosity[12, 13]
The results show that the ANN model predicts the μnf of the compound with great accuracy
Summary
An ANN is a powerful tool for processing raw data inspired by human brain structure and consists of many neurons that collaborate to model a s ystem[62]. For training an ANN, the first step is to create a database of experimental or simulation patterns to feed the network for learning. To this end, 203 different samples in terms of temperature and φ were prepared for MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid. As indicated previously, determining the number of neurons in the hidden layer has an important influence on the modeling performance; various topologies (number of neurons in hidden layer and transfer function) were considered for each ANN, and the best combination is determined trial and error. The best performance is obtained using 10 neurons with tangent-hyperbolic sigmoid function in the second and linear transfer output layer.
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