Data-driven turbulence modelling for magnetohydrodynamic flows in annular pipes
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912242067308544 |
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| author | Santamaria, Alejandro Montoya Buchanan, Tyler Fico, Francesco Langella, Ivan Dwight, Richard P. Doan, Nguyen Anh Khoa |
| author_facet | Santamaria, Alejandro Montoya Buchanan, Tyler Fico, Francesco Langella, Ivan Dwight, Richard P. Doan, Nguyen Anh Khoa |
| contents | We present a data-driven approach to Reynolds-averaged Navier-Stokes turbulence closure modelling in magnetohydrodynamic (MHD) flows. In these flows the magnetic field interacting with the conductive fluid induces unconventional turbulence states such as quasi two-dimensional (2D) turbulence, and turbulence suppression, which are poorly represented by standard Boussinesq models. Our data-driven approach uses time-averaged Large Eddy Simulation (LES) data of annular pipe flows, at different Hartmann numbers, to derive corrections for the $k$-$ω$ SST model. Correction fields are obtained by injecting time averaged LES fields into the MHD RANS equations, and examining the remaining residuals. The correction to the Reynolds-stress anisotropy is approximated with a modified Tensor Basis Neural Network (TBNN). We extend the generalised eddy hypothesis with a traceless antisymmetric tensor representation of the Lorentz force to obtain MHD flow features, thus keeping Galilean and frame invariance while including MHD effects in the turbulence model. The resulting data-driven models are shown to reduce errors in the mean flow, and to generalise to annular flow cases with different Hartmann numbers from those of the training cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven turbulence modelling for magnetohydrodynamic flows in annular pipes Santamaria, Alejandro Montoya Buchanan, Tyler Fico, Francesco Langella, Ivan Dwight, Richard P. Doan, Nguyen Anh Khoa Fluid Dynamics We present a data-driven approach to Reynolds-averaged Navier-Stokes turbulence closure modelling in magnetohydrodynamic (MHD) flows. In these flows the magnetic field interacting with the conductive fluid induces unconventional turbulence states such as quasi two-dimensional (2D) turbulence, and turbulence suppression, which are poorly represented by standard Boussinesq models. Our data-driven approach uses time-averaged Large Eddy Simulation (LES) data of annular pipe flows, at different Hartmann numbers, to derive corrections for the $k$-$ω$ SST model. Correction fields are obtained by injecting time averaged LES fields into the MHD RANS equations, and examining the remaining residuals. The correction to the Reynolds-stress anisotropy is approximated with a modified Tensor Basis Neural Network (TBNN). We extend the generalised eddy hypothesis with a traceless antisymmetric tensor representation of the Lorentz force to obtain MHD flow features, thus keeping Galilean and frame invariance while including MHD effects in the turbulence model. The resulting data-driven models are shown to reduce errors in the mean flow, and to generalise to annular flow cases with different Hartmann numbers from those of the training cases. |
| title | Data-driven turbulence modelling for magnetohydrodynamic flows in annular pipes |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2502.16245 |