This study assesses various subgrid-scale models within the framework of Large Eddy Simulation (LES) using a remeshed Vortex method (RVM). RVM is a semi-Lagrangian method discretizing the vorticity-velocity Navier–Stokes equations that has proven to be a stable and less dissipative alternative to more classical Eulerian methods. The subgrid-scale models are first tested on the well-known Taylor–Green Vortex case at Re=5000. Notably, the Variational Multiscale (VMS) variant of the Smagorinsky model and the Spectral Vanishing Viscosity (SVV) approaches emerge as the best-suited to the RVM, as they add diffusion to only the smallest resolved vorticity scales. Then, a stochastic uncertainty quantification analysis is conducted for both selected models, and the model coefficients are calibrated against direct numerical simulation. These coefficients are then applied to additional cases (different regimes, grid resolutions and test cases), showing the robustness of the calibration within the RVM-LES framework.

Large-Eddy Simulations with remeshed Vortex methods: An assessment and calibration of subgrid-scale models

Mariotti A.
Penultimo
;
Salvetti M. V.
Ultimo
2024-01-01

Abstract

This study assesses various subgrid-scale models within the framework of Large Eddy Simulation (LES) using a remeshed Vortex method (RVM). RVM is a semi-Lagrangian method discretizing the vorticity-velocity Navier–Stokes equations that has proven to be a stable and less dissipative alternative to more classical Eulerian methods. The subgrid-scale models are first tested on the well-known Taylor–Green Vortex case at Re=5000. Notably, the Variational Multiscale (VMS) variant of the Smagorinsky model and the Spectral Vanishing Viscosity (SVV) approaches emerge as the best-suited to the RVM, as they add diffusion to only the smallest resolved vorticity scales. Then, a stochastic uncertainty quantification analysis is conducted for both selected models, and the model coefficients are calibrated against direct numerical simulation. These coefficients are then applied to additional cases (different regimes, grid resolutions and test cases), showing the robustness of the calibration within the RVM-LES framework.
2024
de Crouy-Chanel, M.; Mimeau, C.; Mortazavi, I.; Mariotti, A.; Salvetti, M. V.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1236667
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