A Numerical Proof of Shell Model Turbulence Closure
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913403735375872 |
|---|---|
| author | Ortali, Giulio Corbetta, Alessandro Rozza, Gianluigi Toschi, Federico |
| author_facet | Ortali, Giulio Corbetta, Alessandro Rozza, Gianluigi Toschi, Federico |
| contents | The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure, based on deep recurrent neural networks, that quantitatively reproduces, within statistical errors, Eulerian and Lagrangian structure functions and the intermittent statistics of the energy cascade, including those of subgrid fluxes. To achieve high-order statistical accuracy, and thus a stringent statistical test, we employ shell models of turbulence. Our results encourage the development of similar approaches for 3D Navier-Stokes turbulence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_09289 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Numerical Proof of Shell Model Turbulence Closure Ortali, Giulio Corbetta, Alessandro Rozza, Gianluigi Toschi, Federico Fluid Dynamics Statistical Mechanics Machine Learning Chaotic Dynamics Computational Physics The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure, based on deep recurrent neural networks, that quantitatively reproduces, within statistical errors, Eulerian and Lagrangian structure functions and the intermittent statistics of the energy cascade, including those of subgrid fluxes. To achieve high-order statistical accuracy, and thus a stringent statistical test, we employ shell models of turbulence. Our results encourage the development of similar approaches for 3D Navier-Stokes turbulence. |
| title | A Numerical Proof of Shell Model Turbulence Closure |
| topic | Fluid Dynamics Statistical Mechanics Machine Learning Chaotic Dynamics Computational Physics |
| url | https://arxiv.org/abs/2202.09289 |