Investigation of wall-function approximations and subgrid-scale models in large eddy simulation of separated flow in a channel with streamwise periodic constrictions
By L. Temmerman, M. Leschziner, C. Mellen, Jochen Fröhlich. 2003
269 citations recorded by the source corpus.
Subfields: Computational Mechanics, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/15fc71292e7d3433dc79053f3810e47606076ffb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Nikolaus Peller
- C. Rapp
- E. Touber
- M. Leschziner
- Michael Breuer
- Michael T. Manhart
- Jochen Fröhlich
- Gregor J. Gassner
In Environmental Engineering
- M. Leschziner
- E. Touber
- Jochen Fröhlich
- Nicholas Geneva
- Nicholas Zabaras
- Wolfgang Rodi
- Heng Xiao
- Jin-Long Wu
Citations in the corpus
- Flow over periodic hills – Numerical and experimental study in a wide range of Reynolds numbers (2009, methodology): “[48] investigated the predictive accuracy of different wall models based on this case.”
- Highly resolved large-eddy simulation of separated flow in a channel with streamwise periodic constrictions (2005, methodology): “For the geometry considered in the present study, simulations with relatively coarse grids (Temmerman et al. 2003) as well as RANS studies (Manceau & Bonnet 2003) indicate that a 1% change in the predicted mean separation location goes hand-inhand with a 7% change in the reattachment location, with…”
- Near-wall streak modification by spanwise oscillatory wall motion and drag-reduction mechanisms (2012, methodology): “All simulations contributing to the present study have been performed with a variant of the general non-orthogonal-grid, block-structured, finite-volume method based on a fully co-located storage and realized in the in-house code STREAMLES for incompressible flows (Temmerman et al. 2003).”
- High‐order discontinuous Galerkin spectral element methods for transitional and turbulent flow simulations (2014, background): “The recirculation bubble in between is known to be strongly dependent on separation effects [59].”
- Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018, background): “Figure 6: The learned mixing coefficients of the training neural network for flow over periodic hills [53, 54] at bulk Reynolds number 6210.”