Prediction of rheology of shear thickening fluids using phenomenological and artificial neural network models

摘要

Prediction models for the viscosity curve of a shear thickening fluid (STF) over a wide range of shear rate at different temperatures were developed using phenomenological and artificial neural network (ANN) models. STF containing 65% (w/w) silica nanoparticles was prepared using polyethylene glycol (PEG) as dispersion medium, and tested for rheological behavior at different temperatures. The experimental data set was divided into training data and testing data for the model development and validation, respectively. For both the models, the viscosity of STF was estimated for all the zones with good fit between experimental and predicted viscosity, for both training and testing data sets.