Hostname: page-component-745bb68f8f-lrblm Total loading time: 0 Render date: 2025-02-11T12:09:04.014Z Has data issue: false hasContentIssue false
  • English
  • Français

Stochastic backscatter in large-eddy simulations of boundary layers

Published online by Cambridge University Press:  26 April 2006

P. J. Mason  and
D. J. Thomson
P. J. Mason
Affiliation:
The Meteorological Office, London Road, Bracknell, Berks. RG12 2SZ. UK
D. J. Thomson
Affiliation:
The Meteorological Office, London Road, Bracknell, Berks. RG12 2SZ. UK
Get access Rights & Permissions [Opens in a new window]

Abstract

The ability of a large-eddy simulation to represent the large-scale motions in the interior of a turbulent flow is well established. However, concerns remain for the behaviour close to rigid surfaces where, with the exception of low-Reynolds-number flows, the large-eddy description must be matched to some description of the flow in which all except the larger-scale ‘inactive’ motions are averaged. The performance of large-eddy simulations in this near-surface region is investigated and it is pointed out that in previous simulations the mean velocity profile in the matching region has not had a logarithmic form. A number of new simulations are conducted with the Smagorinsky (1963) subgrid model. These also show departures from the logarithmic profile and suggest that it may not be possible to eliminate the error by adjustments of the subgrid lengthscale. An obvious defect of the Smagorinsky model is its failure to represent stochastic subgrid stress variations. It is shown that inclusion of these variations leads to a marked improvement in the near-wall flow simulation. The constant of proportionality between the magnitude of the fluctuations in stress and the Smagorinsky stresses has been empirically determined to give an accurate logarithmic flow profile. This value provides an energy backscatter rate slightly larger than the dissipation rate and equal to idealized theoretical predictions (Chasnov 1991).

Type
Research Article
Information
Journal of Fluid Mechanics , Volume 242 , September 1992 , pp. 51 - 78
Copyright
© 1992 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bardina, J., Ferziger, J. H. & Reynolds, W. C. 1983 Improved turbulence models based on large eddy simulation of homogeneous, incompressible, turbulent flows. Rep. TF-19. Thermosciences Division, Department of Mechanical Engineering. Stanford University.
Chasnov, J. R. 1991 Simulation of the Kolmogorov inertial subrange using an improved subgrid model.. Phys. Fluids A 3, 188–200.Google Scholar
Deardorff, J. W. 1970 A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers. J. Fluid Mech. 41, 453–480.Google Scholar
Grant, A. L. M. 1986 Observations of boundary layer structure made during the KONTUR experiment. Q. J. R. Met. Soc. 112, 825–841.Google Scholar
Högström, U. 1990 Analysis of turbulence structure in the surface layer with a modified similarity formulation for near neutral conditions. J. Atmos. Sci. 47. 1949–1972.Google Scholar
Kaimal, J. C., Wyngaard, J. C., Izumi, Y. & Coté, O. R. 1972 Spectral characteristics of surface layer turbulence. Q. J. R. Met. Soc. 98, 563–589.Google Scholar
Kraichnan, R. H. 1976 Eddy viscosity in two and three-dimensions. J. Atmos. Sci. 33, 1521–1536.Google Scholar
Leith, C. E. 1990 Stochastic backscatter in a subgrid-scale model: plane shear mixing layer.. Phys. Fluids A 2, 297–299.Google Scholar
Lesieur, M. 1987 Turbulence in Fluids. Martinus Nijhoff.
Leslie, D. C. & Quarini, G. L. 1979 The application of turbulence theory to the formulation of subgrid modelling procedures. J. Fluid Mech. 91, 65–91.Google Scholar
Lilly, D. K. 1967 The representation of small-scale turbulence in numerical simulation experiments. Proc. Tenth IBM Scientific Computing Symp. on Environmental Sciences, Thomas J. Watson Research Center, Yorktown Heights, pp. 195–210.
Mason, P. J. 1989 Large-eddy simulation of the convective atmospheric boundary layer. J. Atmos. Sci. 46, 1492–1516.Google Scholar
Mason, P. J. & Callen, N. S. 1986 On the magnitude of the subgrid-scale eddy coefficient in large-eddy simulations of turbulent channel flow. J. Fluid Mech. 162, 439–462.Google Scholar
Mason, P. J. & Thomson, D. J. 1987 Large-eddy simulations of the neutral-static-stability planetary boundary layer. Q. J. R. Met. Soc. 113, 413–443.Google Scholar
Moin, P. & Kim, J. 1982 Numerical investigations of turbulent channel flow. J. Fluid Mech. 118, 341–377.Google Scholar
Piacsek, S. A. & Williams, G. P. 1970 Conservation properties of convection difference schemes. J. Comput. Phys. 6, 392–405.Google Scholar
Piomelli, U., Moin, P. & Ferziger, J. H. 1988 Model consistency in large eddy simulation of turbulent channel flows. Phys. Fluids 31, 1884–1891.Google Scholar
Smagorinsky, J. 1963 General circulation experiments with the primitive equations: 1. The basic experiment. Mon. Weather Rev. 91, 99–164.2.3.CO;2>CrossRefGoogle Scholar
Smith, S. D. & Anderson, R. J. 1984 Spectra of humidity, temperature and wind over the sea at Sable Island, Nova Scotia. J. Geophys. Res. 89, 2029–2040.Google Scholar
Wood, N. & Mason, P. J. 1991 The influence of static stability on the effective roughness lengths for momentum and heat transfer. Q. J. R. Met. Soc. 117, 1025–1056.Google Scholar