Deep learning of vortex-induced vibrations

• Published in: Journal of fluid mechanics 2019-02, Vol.861, p.119-137
• Main Authors: Raissi, Maziar, Wang, Zhicheng, Triantafyllou, Michael S., Karniadakis, George Em
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bluff bodies; Computational fluid dynamics; Computer applications; Data; Deep learning; Drag; Dyes; Dynamics; Equations of motion; Fields (mathematics); Flow control; Fluid dynamics; Fluid flow; Fluid mechanics; Fluids; Hydrodynamics; Incompressible flow; Inverse problems; JFM Papers; Movement; Navier-Stokes equations; Neural networks; Optimization techniques; Physics; Pressure; Pressure dependence; Pressure field; Problems; Resonant frequencies; Resonant frequency; Reynolds number; Smoke; System identification; Time dependence; Training; Velocity; Velocity distribution; Vibrations; Vortex shedding; Vortex-induced vibrations; Vortices
• ISSN: 0022-1120; 1469-7645
• DOI: 10.1017/jfm.2018.872

Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.