Dynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical
properties of fluids containing nanostructures, these types of fluids are widely employed
in thermal mediums. The nanofluid’s dynamic viscosity relies on different variables including size of
solid phase, concentration and temperature. In the present study, three algorithms including multivariable
polynomial regression (MPR), artificial neural network–multilayer perceptron (ANN-MLP)
and multivariate adaptive regression splines (MARS) are applied to model the dynamic viscosity of
silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data
extraction. The input variables considered in the modeling process to be the most important ones
are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid.
The R2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR
algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective
and the dominant parameter for the dynamic viscosity of the nanofluid in comparison with size
and concentration.
