PREDICTION OF DYNAMIC VISCOSITY OF A NEW NON-NEWTONIAN HYBRID NANOFLUID USING EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK (ANN) METHODS
Davood Toghraie, Nima Sina, Milad Mozafarifard, As’ad Alizadeh, Farid Soltani, Mohammad Ali Fazilati
Abstract
In this paper, an artificial neural network (ANN) has been studied for the viscosity of MWCNTs-ZnO/water-ethylene glycol (80:20 vol.%) non-Newtonian nanofluid. To evaluate the rheological behavior of the nanocoolants, for each solid volume fraction and temperature, all experiments were repeated at different shear rates. After generating the experimental data, an ANN method is applied. The ANN is selected based on the different generating architectures (neuron numbers). The algorithm for choosing the best ANN is presented. Also, using the correlation method, the viscosity of nanofluid is predicted. Finally, ANN and correlation results are compared with the obtained data from the correlation method. It was found that the ANN had a better ability in predicting the viscosity of nanofluid compared with the correlation method because the (MSE) of ANN was 0.0885, and the MSE of the correlation method was 0.9531. However, both approaches are useful, but ANN had a better ability to model the viscosity of nanofluid based on the input values.