Building-block flow model for computational fluids
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910488409931776 |
|---|---|
| author | Arranz, Gonzalo Ling, Yuenong Costa, Sam Goc, Konrad Lozano-Duran, Adrian |
| author_facet | Arranz, Gonzalo Ling, Yuenong Costa, Sam Goc, Konrad Lozano-Duran, Adrian |
| contents | We introduce a closure model for wall-modeled large-eddy simulation (WMLES), referred to as the Building-block Flow Model (BFM). The foundation of the model rests on the premise that a finite collection of simple flows encapsulates the essential physics necessary to predict more complex scenarios. The BFM is implemented using artificial neural networks and introduces five advancements within the framework of WMLES: (1) It is designed to predict multiple flow regimes (wall turbulence under zero, favorable, adverse mean-pressure-gradient, and separation); (2) It unifies the closure model at solid boundaries (i.e., the wall model) and the rest of the flow (i.e., the subgrid-scale model) into a single entity; (3) It ensures consistency with numerical schemes and gridding strategy by accounting for numerical errors; (4) It is directly applicable to arbitrary complex geometries; (5) It can be scaled up to model additional flow physics in the future if needed (e.g., shockwaves and laminar-to-turbulent transition). The BFM is utilized to predict key quantities of interest in turbulent channel and pipe flows, a Gaussian bump, a simplified aircraft, and a realistic aircraft in landing configuration. In all cases, the BFM demonstrates similar or superior capabilities in terms of accuracy and computational efficiency compared to previous state-of-the-art closure models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building-block flow model for computational fluids Arranz, Gonzalo Ling, Yuenong Costa, Sam Goc, Konrad Lozano-Duran, Adrian Fluid Dynamics Computational Physics We introduce a closure model for wall-modeled large-eddy simulation (WMLES), referred to as the Building-block Flow Model (BFM). The foundation of the model rests on the premise that a finite collection of simple flows encapsulates the essential physics necessary to predict more complex scenarios. The BFM is implemented using artificial neural networks and introduces five advancements within the framework of WMLES: (1) It is designed to predict multiple flow regimes (wall turbulence under zero, favorable, adverse mean-pressure-gradient, and separation); (2) It unifies the closure model at solid boundaries (i.e., the wall model) and the rest of the flow (i.e., the subgrid-scale model) into a single entity; (3) It ensures consistency with numerical schemes and gridding strategy by accounting for numerical errors; (4) It is directly applicable to arbitrary complex geometries; (5) It can be scaled up to model additional flow physics in the future if needed (e.g., shockwaves and laminar-to-turbulent transition). The BFM is utilized to predict key quantities of interest in turbulent channel and pipe flows, a Gaussian bump, a simplified aircraft, and a realistic aircraft in landing configuration. In all cases, the BFM demonstrates similar or superior capabilities in terms of accuracy and computational efficiency compared to previous state-of-the-art closure models. |
| title | Building-block flow model for computational fluids |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2403.09000 |