Enhancement of an industrial finite-volume code for large-eddy-type simulation of incompressible high Reynolds number flow using near-wall modelling
| Autor: | Gert Lube, Tobias Knopp, Roland Kessler, Xiaoqin Zhang |
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| Rok vydání: | 2010 |
| Předmět: |
turbulent channel flow
Computational Mechanics General Physics and Astronomy Geometry 01 natural sciences 010305 fluids & plasmas Pipe flow Physics::Fluid Dynamics Flow separation large-eddy simulation Incompressible flow convergence study wall-function 0103 physical sciences 0101 mathematics Mathematics Turbulence Mechanical Engineering backward-facing step Mechanics Computer Science Applications 010101 applied mathematics Mechanics of Materials Turbulence kinetic energy Detached eddy simulation grid refinement Reynolds-averaged Navier–Stokes equations Large eddy simulation |
| Zdroj: | Computer Methods in Applied Mechanics and Engineering. 199:890-902 |
| ISSN: | 0045-7825 |
| DOI: | 10.1016/j.cma.2009.01.005 |
| Popis: | We present a validation strategy for enhancement of an unstructured industrial finite-volume solver designed for steady RANS problems for large-eddy-type simulation with near-wall modelling of incompressible high Reynolds number flow. Different parts of the projection-based discretisation are investigated to ensure LES capability of the numerical method. Turbulence model parameters are calibrated by using a minimisation of least-squares functionals for first and second order statistics of the basic benchmark problems decaying homogeneous turbulence and turbulent channel flow. Then the method is applied to the flow over a backward facing step at Reh = 37,500. Of special interest is the role of the spatial and temporal discretisation error for low order schemes. For wall-bounded flows, present results confirm existing best practice guidelines for mesh design. For free-shear layers, a sensor to quantify the resolution quality of the LES based on the resolved turbulent kinetic energy is presented and applied to the flow over a backward facing step at Reh = 37,500. |
| Databáze: | OpenAIRE |
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