A Meta-Model to Predict the Drag Coefficient of a Particle Translating in Viscoelastic Fluids: A Machine Learning Approach.

This study presents a framework based on Machine Learning (ML) models to predict the drag coefficient of a spherical particle translating in viscoelastic fluids. For the purpose of training and testing the ML models, two datasets were generated using direct numerical simulations (DNSs) for the viscoelastic unbounded flow of Oldroyd-B ( containing 12,120 data points) and Giesekus ( containing 4950 data points) fluids past a spherical particle. The kinematic input features were selected to be Reynolds number, 0 View Article and Find Full Text PDF