2010
DOI: 10.1016/j.cam.2009.06.005
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Finite element LES and VMS methods on tetrahedral meshes

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Cited by 10 publications
(5 citation statements)
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“…The main difference between LES and VMS is that the latter projects the scales into the function spaces rather than filtering them by spatial averaging. Thus the VMS method avoids the problems of spatial filtering, spatial differentiation in bounded domains as well as with nonconstant filter widths and the choosing the correct boundary conditions for spatially averaging at large scales (John et al, 2010).…”
Section: Variational Multiscale Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The main difference between LES and VMS is that the latter projects the scales into the function spaces rather than filtering them by spatial averaging. Thus the VMS method avoids the problems of spatial filtering, spatial differentiation in bounded domains as well as with nonconstant filter widths and the choosing the correct boundary conditions for spatially averaging at large scales (John et al, 2010).…”
Section: Variational Multiscale Methodsmentioning
confidence: 99%
“…The linearized system is solved using the generalized minimal residual Krylov subspace method (Kelley, 1995) with an incomplete LU (lower upper) factorization of the system matrix deployed as a preconditioning step.…”
Section: Variational Multiscale Methodsmentioning
confidence: 99%
“…The use of the three-scale projection-based VMS method with adaptive choice of the projection space on tetrahedral grids with the Bernardi-Raugel element [17] is described and studied in [100].…”
Section: Experience In Numerical Simulationsmentioning
confidence: 99%
“…The main difference between LES and VMS is that the latter projects the scales into the function spaces rather than filtering them by spatial averaging. Thus the VMS-method avoids the problems of spatial filtering, spatial differentiation in bounded domains as well as with non-constant filter widths, and the issue of choosing the correct boundary conditions for spatially averaging at large scales (John et al, 2010).…”
Section: Variational Multiscale Methodsmentioning
confidence: 99%