Hybrid LES/RANS for flows including separation: A new wall function using Machine Learning based on binary search trees
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909519215329280 |
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| author | Davidson, Lars |
| author_facet | Davidson, Lars |
| contents | Machine Learning (ML) is used for developing wall functions for Improved Delayed Detached Eddy Simulations (IDDES). The ML model is based on KDtree which essentially is a fast look-up table. It searches the nearest target datapoint(s) for which y+ and U+ are closest to the CFD y+ and U+ cells. The target y+ value gives the friction velocity which is used for setting the wall shear stress for the wall-parallel velocity and for fixing k and epsilon at the wall-adjacent cells. Two target databases are created from time-averaged data of low-Reynolds number (i.e. wall-resolved) IDDES: diffuser flow with opening angle alpha=15 degrees and hump flow.
The new ML wall function is used to predict five test cases: diffuser flow with opening angles alpha=15 degrees and alpha=10 degrees the hump flow, channel flow at $Re_tau=16 000 and flat-plate boundary layer.
A novel grid strategy is used. The wall-adjacent cells are large (20 < y+ < 60 in attached boundary layers). But further away from the wall, the wall-normal cell distribution is identical to that of a low-Re number grid. This new grid is found to improve the predictions compared to a standard wall-function grid.
It is found that the number of cells for a wall-resolved IDDES grid (grid stretching 15%) is a factor of 0.2ln(Re_tau) larger than that of a standard wall-functions mesh (constant wall-normal grid cells).
The new ML wall function is found to perform well compared to the low-Re IDDES and better than the Reichardt's wall function |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid LES/RANS for flows including separation: A new wall function using Machine Learning based on binary search trees Davidson, Lars Fluid Dynamics Machine Learning (ML) is used for developing wall functions for Improved Delayed Detached Eddy Simulations (IDDES). The ML model is based on KDtree which essentially is a fast look-up table. It searches the nearest target datapoint(s) for which y+ and U+ are closest to the CFD y+ and U+ cells. The target y+ value gives the friction velocity which is used for setting the wall shear stress for the wall-parallel velocity and for fixing k and epsilon at the wall-adjacent cells. Two target databases are created from time-averaged data of low-Reynolds number (i.e. wall-resolved) IDDES: diffuser flow with opening angle alpha=15 degrees and hump flow. The new ML wall function is used to predict five test cases: diffuser flow with opening angles alpha=15 degrees and alpha=10 degrees the hump flow, channel flow at $Re_tau=16 000 and flat-plate boundary layer. A novel grid strategy is used. The wall-adjacent cells are large (20 < y+ < 60 in attached boundary layers). But further away from the wall, the wall-normal cell distribution is identical to that of a low-Re number grid. This new grid is found to improve the predictions compared to a standard wall-function grid. It is found that the number of cells for a wall-resolved IDDES grid (grid stretching 15%) is a factor of 0.2ln(Re_tau) larger than that of a standard wall-functions mesh (constant wall-normal grid cells). The new ML wall function is found to perform well compared to the low-Re IDDES and better than the Reichardt's wall function |
| title | Hybrid LES/RANS for flows including separation: A new wall function using Machine Learning based on binary search trees |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2410.17767 |