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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… Click to show full abstract
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 (OB-set containing 12,120 data points) and Giesekus (GI-set containing 4950 data points) fluids past a spherical particle. The kinematic input features were selected to be Reynolds number, 0
Keywords:
predict drag;
machine learning;
model;
meta model;
particle
Journal Title: Polymers
Year Published: 2022
Link to full text (if available)
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Journal Title: Polymers
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!