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Length-scale distribution functions and conditional means for various fields in turbulence

Published online by Cambridge University Press:  11 July 2008

LIPO WANG
Affiliation:
Institut für Technische Verbrennung, RWTH-Aachen, Templergraben 64, Aachen, Germany
NORBERT PETERS
Affiliation:
Institut für Technische Verbrennung, RWTH-Aachen, Templergraben 64, Aachen, Germany

Abstract

Dissipation elements are identified for various direct numerical simulation (DNS) fields of homogeneous shear turbulence. The fields are those of the fluctuations of a passive scalar, of the three components of velocity and vorticity, of the second invariant of the velocity gradient tensor, turbulent kinetic energy and viscous dissipation. In each of these fields trajectories starting from every grid point are calculated in the direction of ascending and descending gradients, reaching a local maximum and minimum point, respectively. Dissipation elements are defined as spatial regions containing all the grid points from which the same pair of minimum and maximum points is reached. They are parameterized by the linear length between these points and the difference of the field variable at these points.

In analysing the changes that occur during one time step in the linear length as well as in the number of grid points contained in the elements, it is found that rapid splitting and attachment processes occur between elements. These processes are much more frequent than the previously identified processes of cutting and reconnection. The model for the length-scale distribution function that had previously been proposed is modified to include these additional processes. Comparisons of the length-scale distribution function for the various fields with the proposed model show satisfactory agreement.

The conditional mean difference of the field variable at the minimum and maximum points of dissipation elements is calculated for the passive scalar field and the three components of velocity. While the conditional mean difference follows the 1/3 inertial-range Kolmogorov scaling for the passive scalar field, the scaling exponent differs from the 1/3 law for each of the three components of velocity. This is thought to be due to the relatively high shear rate of the DNS calculations.

The conditional mean viscous dissipation shows, differently from all other field variables analysed, a pronounced dependence on the linear length of elements. This is explained by intermittency. This finding is used to evaluate the production and the dissipation term of the empirically derived ϵ-equation that is often used in engineering calculations.

Type
Papers
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Bermejo-Moreno, I. & Pullin, D. I. 2008 On the non-local geometry of turbulence. J. Fluid Mech. 603, 101135.CrossRefGoogle Scholar
Bradshaw, P., Ferris, D. H. & Atwell, N. P. 1967 Calculation of boundary-layer development using the turbulent energy equation. J. Fluid Mech. 28, 593616.CrossRefGoogle Scholar
Chertkov, M. & Pumir, I. S. 2000 Statistical geometry and Lagrangian dynamics in turbulence. In Itermittency in Turbulent Flows (ed. Vassilicos, J. C.), pp. 243261. Cambridge University Press.Google Scholar
Frisch, U. 1995 Turbulence: The Legacy of A.N. Kolmogorov. Cambridge University Press.CrossRefGoogle Scholar
Gibson, C. H. 1968 Fine structure of scalar fields mixed by turbulence I. Zero gradient points and minimal gradient surfaces. Phys. Fluids 11, 23052315.CrossRefGoogle Scholar
von Kármán, T. & Howard, L. 1938 On the statistical theory of isotropic turbulence. Proc. R. Soc. Lond. 164, 192215.Google Scholar
Kolmogorov, A. N. 1941 a Dissipation of energy under locally isotropic turbulence. Dokl. Akad. Nauk SSSR 32, 1618.Google Scholar
Kolmogorov, A. N. 1941 b The local structure of turbulence in an incompressible viscous fluid for very large Reynolds numbers. Dokl. Akad. Nauk SSSR 30, 301305.Google Scholar
Kolmogorov, A. N. 1962 A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number. J. Fluid Mech. 13, 8285.CrossRefGoogle Scholar
Meneveau, C. & Sreenivasan, K. R. 1991 The multifractional nature of turbulent energy dissipation. J. Fluid Mech. 224, 429484.Google Scholar
Mydlarski, L. 2003 Mixed velocity-passive scalar statistics in high-Reynolds-number turbulence. J. Fluid Mech. 475, 173203.Google Scholar
Obukhov, A. R. 1962 Some specific features of atmospheric turbulence. J. Fluid Mech. 13, 7781.Google Scholar
Peters, N. & Wang, L. 2006 Dissipation element analysis of scalar fields in turbulence. C. R. Mech. 334, 433506.Google Scholar
Pope, S. 2000 Turbulent Flows. Cambridge University Press.CrossRefGoogle Scholar
Richardson, L. 1922 Weather Prediction by Numerical Process. Cambridge University Press.Google Scholar
Saddoughi, S. G. & Veeravalli, S. V. 1994 Local isotropy in turbulent boundary layers at high Reynolds number. J. Fluid Mech. 268, 333372.CrossRefGoogle Scholar
Sreenivasan, K. R. & Antonia, R. A. 1997 The phenomenology of small-scale turbulence. Annu. Rev. Fluid Mech. 29, 435472.CrossRefGoogle Scholar
Tsinober, A. 2001 An Informal Introduction to Turbulence. Kluwer.Google Scholar
Wang, L. & Peters, N. 2006 The length scale distribution function of the distance between extremal points in passive scalar turbulence. J. Fluid Mech. 554, 457475.Google Scholar