Year: 2024
Type: article
Abstract: The dynamics of flexible filaments entrained in flow are important for understanding many biological and industrial processes. This work describes a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning; the technique is applied to sedimentation in a quiescent, viscous Newtonian fluid, using results from detailed simulations as the data set. Over a wide range of fiber flexibilities, the filament shape dynamics can be represented with high accuracy with only four degrees of freedom.
Source: Physical Review Fluids
Authors: Andrew J. Fox, Michael D. Graham
Institution: University of Wisconsin–Madison
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: Office of Naval Research
Award: G456702614
OpenAlex ID: W4401638102