Abstract:
In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles
into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The
first technique employed the response surface methodology based on the design of experiments,
while in the second technique, artificial intelligence algorithms were implemented to estimate
the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2,
and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more
nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering
the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction
were effective on the target variable and were defined in the implementation of both techniques.
Considering two constraints (i.e., the maximum R-square value and the minimum mean square
error), the best neural network and suitable polynomial were selected. Finally, a comparison was
made between the two techniques to evaluate their potential in viscosity estimation. Statistical
considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944,
respectively, which is an indication of the superiority of the ANN technique to the RSM one.
