Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity

This paper presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of oxide nanoparticles suspension in water and ethylene glycol. The model accounts for the effect of temperature, nanoparticle volume fraction, nanoparticle diameter, cluster of nanoparticles average size, and base fluid properties. The model was trained on a set of data obtained by the present authors and tested on data coming from other authors. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 4.15%. The characteristic parameters of the ANN model are reported in details in the paper.