Precise smart model for estimating dynamic viscosity of SiO₂/ethylene glycol–water nanofluid

Artificial neural network (ANN) is widely being used in engineering applications in order to provide predicting models to estimate the performance of the studied system under specific working conditions. One of the significant characteristics that are highly practical in fluid mechanics and heat transfer systems is the dynamic viscosity which highly affects pressure drop and also has an influence on the heat transfer performance. Due to the lack of a precise model to predict the dynamic viscosity, in this research, experimentally measured dynamic viscosity of SiO₂/ethylene glycol–water nanofluid data is collected from the literature and used to present a smart model based on the ANN technique. In order to provide a precise smart model, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms are applied in the neural network. The accuracy of the proposed model is validated through performing error analysis. It is monitored that the employed approach is highly potent in estimating high accuracy responses since the results of mean square and correlation coefficient analyses are 5.5 and 0.998 Pa s.