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

Autor: Faroughi SA; Geo-Intelligence Laboratory, Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA., Roriz AI; Department of Polymer Engineering, Institute for Polymers and Composites (IPC), Campus of Azurém, Engineering School of the University of Minho, 4800-058 Guimarães, Portugal., Fernandes C; Geo-Intelligence Laboratory, Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA.; Department of Polymer Engineering, Institute for Polymers and Composites (IPC), Campus of Azurém, Engineering School of the University of Minho, 4800-058 Guimarães, Portugal.
Jazyk: angličtina
Zdroj: Polymers [Polymers (Basel)] 2022 Jan 21; Vol. 14 (3). Date of Electronic Publication: 2022 Jan 21.
DOI: 10.3390/polym14030430
Abstrakt: 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
Databáze: MEDLINE
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