Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-23T15:34:34.888Z Has data issue: false hasContentIssue false

Realizing turbulent statistics

Published online by Cambridge University Press:  18 April 2011

JÉRÔME HŒPFFNER*
Affiliation:
Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan
YOSHITSUGU NAKA
Affiliation:
Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan
KOJI FUKAGATA
Affiliation:
Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan
*
Email address for correspondence: [email protected]

Abstract

How to design an artificial inflow condition in simulations of the Navier–Stokes equation that is already fully turbulent? This is the turbulent inflow problem. This first question is followed by: How much of the true turbulence must be reproduced at the inflow? We present a technique able to produce a random field with the exact two-point two-time covariance of a given reference turbulent flow. It is obtained as the output of a linear filter fed with white noise. The method is illustrated on the simulation of a turbulent free shear layer. The filter coefficients are obtained from the solution of the Yule–Walker equation, and the computation can be performed efficiently using a recursive solution procedure. The method should also be useful in the study of flow receptivity, when the processes of transition to turbulence are sensitive to the perturbation environment.

Type
Papers
Copyright
Copyright © Cambridge University Press 2011

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

REFERENCES

Akaike, H. 1973 Block Toeplitz matrix inversion. SIAM J. Appl. Math. 24 (2), 234241.CrossRefGoogle Scholar
Bamieh, B. & Dahleh, M. 2001 Energy amplification in channel flows with stochastic excitation. Phys. Fluids 13 (11), 32583269.CrossRefGoogle Scholar
Bartels, R. H. & Stewart, G. W. 1972 Solution of the matrix equation AX + XB = C: algorithm 432. Commun. ACM 15, 820826.CrossRefGoogle Scholar
Berkooz, G., Holmes, P. & Lumley, J. L. 1993 The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech. 25, 539575.CrossRefGoogle Scholar
Butler, K. M. & Farrell, B. F. 1992 Three-dimensional optimal perturbations in viscous shear flow. Phys. Fluids 4 (8), 16371650.CrossRefGoogle Scholar
Chevalier, M., Hoepffner, J., Bewley, T. R. & Henningson, D. 2006 State estimation in wall-bounded flow systems. Part II. Turbulent flows. J. Fluid Mech. 552, 167187.CrossRefGoogle Scholar
Chomaz, J.-M. 2005 Global instabilities in spatially developing flows: non-normality and nonlinearity. Annu. Rev. Fluid Mech. 37, 357392.CrossRefGoogle Scholar
Cybenko, G. 1980 The numerical stability of the Levinson–Durbin algorithm for Toeplitz systems of equations. SIAM J. Sci. Stat. Comput. 1 (3), 303319.CrossRefGoogle Scholar
Druault, P., Lardeau, S., Bonnet, J.-P., Coiffet, J., Delville, J., Lamballais, E., Largeau, J.-F. & Perret, L. 2004 Generation of three-dimensional turbulent inlet conditions for large-eddy simulation. AIAA J. 42 (3), 447456.CrossRefGoogle Scholar
Druzhinin, O. A. & Elghobashi, S. E. 2001 Direct numerical simulation of a three-dimensional spatially developing bubble-laden mixing layer with two-way coupling. J. Fluid Mech. 429, 2361.CrossRefGoogle Scholar
Durbin, J. 1960 The fitting of time-series models. Rev. Inst. Intl Stat. 28, 233244.CrossRefGoogle Scholar
Farrell, B. F. & Ioannou, P. J. 1993 Stochastic forcing of the linearized Navier–Stokes equations. Phys. Fluids 5 (11), 26002609.CrossRefGoogle Scholar
Fukagata, K., Kasagi, N. & Koumoutsakos, P. 2006 A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces. Phys. Fluids 18 (051703).Google Scholar
Hayes, M. H. 1996 Statistical Digital Signal Processing and Modeling. Wiley.Google Scholar
Ho, C.-M. & Huerre, P. 1984 Perturbed free shear layers. Annu. Rev. Fluid Mech. 16, 365424.CrossRefGoogle Scholar
Hoepffner, J., Chevalier, M., Bewley, T. R. & Henningson, D. 2005 State estimation in wall-bounded flow systems. Part I. Perturbed laminar flows. J. Fluid Mech. 534, 263294.CrossRefGoogle Scholar
Huerre, P. & Monkevitz, P. A. 1990 Local and global instabilities in spatially developing flows. Annu. Rev. Fluid Mech. 22, 473537.CrossRefGoogle Scholar
Keating, A., Piomelli, U., Balaras, E. & Kaltenbach, H.-J. 2004 A priori and a posteriori tests of inflow conditions for large-eddy simulation. Phys. Fluids 16 (12), 46964712.CrossRefGoogle Scholar
Klein, M., Sadiki, A. & Janicka, J. 2003 A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. J. Comput. Phys. 186, 652665.CrossRefGoogle Scholar
di Mare, L., Klein, M., Jones, W. P. & Janicka, J. 2006 Synthetic turbulence inflow conditions for large-eddy simulation. Phys. Fluids 18, 025107.CrossRefGoogle Scholar
Perret, L., Delville, J., Manceau, R. & Bonnet, J.-P. 2008 Turbulent inflow conditions for large-eddy simulation based on low-order empirical model. Phys. Fluids 20, 075107.CrossRefGoogle Scholar
Reddy, S. C. & Henningson, D. 1993 Energy growth in viscous channel flow. J. Fluid Mech. 252, 209238.CrossRefGoogle Scholar
Schmid, P. & Henningson, D. 2001 Stability and Transition in Shear Flows. Springer.CrossRefGoogle Scholar
Vijayan, R., Poor, V., Moore, J. B. & Goodwin, G. C. 1991 A Levinson-type algorithm for modeling fast-sampled data. IEEE Trans. Autom. Control 36 (3), 314321.CrossRefGoogle Scholar
Whittle, P. 1963 On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrix. Biometrika 50 (1 and 2), 129134.CrossRefGoogle Scholar