Abstract
The objective of the present work is to validate the compressible Large-Eddy Simulation (LES) models implemented in the in house parallel unstructured CFD code TermoFluids. Our research team has implemented and tested several LES models over the past years for the incompressible regimen. In order to be able to solve complex turbulent compressible flows, the models are revisited and modified if necessary. In addition, the performance of the implemented hybrid advection scheme is an issue of interest for the numerical simulation of turbulent compressible flows. The models are tested in the well known turbulent channel flow problem at different compressible regimens.
Highlights
The aim of this work is to validate the compressible extension of the existing incompressible Smagorinsky (SMG), Wall-Adaptive Large-Eddy (WALE) and Variational Multiscale (VMS) turbulence models implemented in the in-house software TermoFluids [1]
Our objective is to study the performance of the Large-Eddy Simulation (LES) methods, along with the hybrid advection scheme, in the turbulent channel flow in order to validate them and explore its application in more complex problems, such as turbulent flows with SBLIs
We explore in this work the ability of the LES models to deal with compressible turbulent flows in different compressible regimens
Summary
The aim of this work is to validate the compressible extension of the existing incompressible Smagorinsky (SMG), Wall-Adaptive Large-Eddy (WALE) and Variational Multiscale (VMS) turbulence models implemented in the in-house software TermoFluids [1]. This models have been tested in several cases, such as the flow over a NACA0012 airfoil [2] [3] and a circular cylinder [4]. The Reynolds Averaged Navier Stokes (RANS) approach has been widely used to overcome DNS restrictions It allows less expensive simulations, since turbulence is not directly resolved, introducing the need of turbulence modeling [5]. The extension of the RANS methods to the compressible solver was already examined in previous works [7], concluding that such approach may lead to unsatisfactory predictions
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