Deep learning of vortex-induced vibrations
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 pro...
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| Vydáno v: | Journal of fluid mechanics Ročník 861; s. 119 - 137 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cambridge, UK
Cambridge University Press
25.02.2019
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| Témata: | |
| ISSN: | 0022-1120, 1469-7645 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Wang, Zhicheng Triantafyllou, Michael S. Karniadakis, George Em Raissi, Maziar |
| Author_xml | – sequence: 1 givenname: Maziar orcidid: 0000-0002-8467-4568 surname: Raissi fullname: Raissi, Maziar email: maziar_raissi@brown.edu organization: Division of Applied Mathematics, Brown University, Providence, RI 02912, USA – sequence: 2 givenname: Zhicheng orcidid: 0000-0002-5856-6459 surname: Wang fullname: Wang, Zhicheng organization: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – sequence: 3 givenname: Michael S. orcidid: 0000-0002-4960-7060 surname: Triantafyllou fullname: Triantafyllou, Michael S. organization: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – sequence: 4 givenname: George Em surname: Karniadakis fullname: Karniadakis, George Em organization: Division of Applied Mathematics, Brown University, Providence, RI 02912, USA |
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| Copyright | 2018 Cambridge University Press |
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| DocumentTitleAlternate | M. Raissi, Z. Wang, M. S. Triantafyllou and G. E. Karniadakis Deep learning of vortex-induced vibrations |
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| SubjectTerms | 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 |
| Title | Deep learning of vortex-induced vibrations |
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