Explicitly filtered large-eddy simulation : with application to grid adaptation and wall modeling
Abstract/Contents
- Abstract
- The governing equations for large-eddy simulation (LES) are derived from the application of a low-pass filter to the Navier-Stokes equations. LES has shown to be a tractable method for the computation of high Reynolds number turbulent flows, primarily because the filtration of the Navier-Stokes equations removes the small scales of motion that would otherwise impose prohibitive resolution requirements. The effect of the scales of motion that are smaller than the filter width on the large, resolved scales are then modeled. In practice, the filter used to derive the LES governing equation is not formally defined and instead, it is assumed that the discretization of LES equation will implicitly act as a low-pass filter. This study investigates an alternative derivation of the LES governing equations that requires the formal definition of the filtration operator, known as explicitly filtered LES. It is shown that decoupling the filtering operation from the underlying grid allows for the isolation of subgrid-scale (SGS) modeling errors from numerical discretization errors. In this grid-independent context, it is demonstrated that standard eddy viscosity models are inaccurate at coarse resolutions. By leveraging the definition of the filtering operator, an SGS model is subsequently derived from a low order perturbation of the explicitly filtered governing equations. LES of canonical wall bounded flows (e.g., channels and ducts) at coarse resolutions validate the improved accuracy of the proposed SGS model. Simulations of practical engineering configurations require the ability to handle complex geometries. Previous explicitly filtered LES calculations have been limited to structured grid discretizations because of the difficulty in constructing a low-pass filter on unstructured grids. The explicitly filtered framework and the proposed SGS model are extended for use in unstructured grid environments through the use of differential filters. Unstructured grids also provide the ability to locally increase resolution in regions of the flow where the SGS model is unable to accurately model the stress provided by the unresolved scales of motion. A novel adaptation technique is suggested where the mesh (and/or filter) is refined in regions of the flow where estimates of the SGS fluctuations are largest. An LES of a three-dimensional stalled diffuser is performed to demonstrate the efficacy of the SGS model based mesh refinement criteria and the capabilities of the differential filters on unstructured grids. Lastly, a dynamic wall boundary condition is derived from the differential filter for wall-modeled large-eddy simulation where the near wall turbulence is not resolved. This differential filter based wall model successfully predicts mean dynamics of both wall-bounded flows (channels) and separating flows in complex geometries (airfoil at near-stall conditions) without the prescription of any ad hoc coefficients or RANS/LES hybridization.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bose, Sanjeeb | |
|---|---|---|
| Associated with | Stanford University, Department of Mechanical Engineering | |
| Primary advisor | Moin, Parviz | |
| Thesis advisor | Moin, Parviz | |
| Thesis advisor | Iaccarino, Gianluca | |
| Thesis advisor | Lele, Sanjiva K. (Sanjiva Keshava), 1958- | |
| Advisor | Iaccarino, Gianluca | |
| Advisor | Lele, Sanjiva K. (Sanjiva Keshava), 1958- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sanjeeb T Bose. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by Sanjeeb T Bose
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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