Hostname: page-component-745bb68f8f-b95js Total loading time: 0 Render date: 2025-01-26T14:47:05.076Z Has data issue: false hasContentIssue false

Spatio-temporal spectra in the logarithmic layer of wall turbulence: large-eddy simulations and simple models

Published online by Cambridge University Press:  13 March 2015

Michael Wilczek*
Affiliation:
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA Max Planck Institute for Dynamics and Self-Organization, D-37077 Göttingen, Germany
Richard J. A. M. Stevens
Affiliation:
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA Department of Science and Technology and J.M. Burgers Center for Fluid Dynamics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Charles Meneveau
Affiliation:
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
*
Email address for correspondence: michael.wilczek@ds.mpg.de

Abstract

Motivated by the need to characterize the spatio-temporal structure of turbulence in wall-bounded flows, we study wavenumber–frequency spectra of the streamwise velocity component based on large-eddy simulation (LES) data. The LES data are used to measure spectra as a function of the two wall-parallel wavenumbers and the frequency in the equilibrium (logarithmic) layer. We then reformulate one of the simplest models that is able to reproduce the observations: the random sweeping model with a Gaussian large-scale fluctuating velocity and with additional mean flow. Comparison with LES data shows that the model captures the observed temporal decorrelation, which is related to the Doppler broadening of frequencies. We furthermore introduce a parameterization for the entire wavenumber–frequency spectrum $E_{11}(k_{1},k_{2},{\it\omega};z)$, where $k_{1}$, $k_{2}$ are the streamwise and spanwise wavenumbers, ${\it\omega}$ is the frequency and $z$ is the distance to the wall. The results are found to be in good agreement with LES data.

Type
Rapids
Copyright
© 2015 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

Albertson, J. D. & Parlange, M. B. 1999 Surface length scales and shear stress: implications for land–atmosphere interaction over complex terrain. Water Resour. Res. 35 (7), 21212132.Google Scholar
Banerjee, T. & Katul, G. G. 2013 Logarithmic scaling in the longitudinal velocity variance explained by a spectral budget. Phys. Fluids 25 (12), 125106.Google Scholar
Bou-Zeid, E., Meneveau, C. & Parlange, M. B. 2005 A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows. Phys. Fluids 17, 025105.Google Scholar
Chen, S. & Kraichnan, R. H. 1989 Sweeping decorrelation in isotropic turbulence. Phys. Fluids A 1 (12), 20192024.Google Scholar
Davidson, P. A., Krogstad, P.-A. & Nickels, T. B. 2006 A refined interpretation of the logarithmic structure function law in wall layer turbulence. Phys. Fluids 18 (6), 065112.Google Scholar
Del Álamo, J. C. & Jiménez, J. 2009 Estimation of turbulent convection velocities and corrections to Taylor’s approximation. J. Fluid Mech. 640, 526.Google Scholar
Fisher, M. J. & Davies, P. O. A. L. 1964 Correlation measurements in a non-frozen pattern of turbulence. J. Fluid Mech. 18, 97116.Google Scholar
George, W. K., Hussein, H. J. & Woodward, S. H.1989 An evaluation of the effect of a fluctuating convection velocity on the validity of Taylor’s hypothesis. In Proceedings of the 10th Australasian Fluid Mech. Conference, University of Melbourne, 1989 (ed. A. E. Perry et al.), vol. II, pp. 11.5–11.8.Google Scholar
He, G.-W., Wang, M. & Lele, S. K. 2004 On the computation of space–time correlations by large-eddy simulation. Phys. Fluids 16 (11), 38593867.Google Scholar
He, G.-W. & Zhang, J.-B. 2006 Elliptic model for space–time correlations in turbulent shear flows. Phys. Rev. E 73, 055303.Google ScholarPubMed
Hultmark, M., Vallikivi, M., Bailey, S. C. C. & Smits, A. J. 2012 Turbulent pipe flow at extreme Reynolds numbers. Phys. Rev. Lett. 108 (9), 94501.CrossRefGoogle ScholarPubMed
Jimenez, J. 2013 Near-wall turbulence. Phys. Fluids 25 (10), 101302.Google Scholar
Katul, G., Parlange, M., Albertson, J. & Chu, C.-R. 1995 The random sweeping decorrelation hypothesis in stratified turbulent flows. Fluid Dyn. Res. 16 (5), 275295.CrossRefGoogle Scholar
Kraichnan, R. H. 1964 Kolmogorov’s hypothesis and Eulerian turbulence theory. Phys. Fluids 7, 17231734.Google Scholar
Krogstad, P. Å., Kaspersen, J. H. & Rimestad, S. 1998 Convection velocities in a turbulent boundary layer. Phys. Fluids 10 (4), 949957.Google Scholar
LeHew, J., Guala, M. & McKeon, B. J. 2011 A study of the three-dimensional spectral energy distribution in a zero pressure gradient turbulent boundary layer. Exp. Fluids 51 (4), 9971012.Google Scholar
Lumley, J. L. 1965 Interpretation of time spectra measured in high-intensity shear flows. Phys. Fluids 8 (6), 10561062.Google Scholar
Mann, J. 1994 The spatial structure of neutral atmospheric surface-layer turbulence. J. Fluid Mech. 273, 141168.Google Scholar
Marusic, I. & Kunkel, G. J. 2003 Streamwise turbulence intensity formulation for flat-plate boundary layers. Phys. Fluids 15, 24612464.Google Scholar
Marusic, I., Monty, J. P., Hultmark, M. & Smits, A. J. 2013 On the logarithmic region in wall turbulence. J. Fluid Mech. 716, R3.Google Scholar
Perry, A. E. & Chong, M. S. 1982 On the mechanism of wall turbulence. J. Fluid Mech. 119, 173217.Google Scholar
Perry, A. E., Henbest, S. & Chong, M. S. 1986 A theoretical and experimental study of wall turbulence. J. Fluid Mech. 165, 163199.CrossRefGoogle Scholar
Porté-Agel, F., Meneveau, C. & Parlange, M. B. 2000 A scale-dependent dynamic model for large-eddy simulation: application to a neutral atmospheric boundary layer. J. Fluid Mech. 415, 261284.Google Scholar
Praskovsky, A. A., Gledzer, E. B., Karyakin, M. Yu. & Zhou, Y. 1993 The sweeping decorrelation hypothesis and energy inertial scale interaction in high Reynolds number flows. J. Fluid Mech. 248, 493511.Google Scholar
Smits, A. J., McKeon, B. J. & Marusic, I. 2011 High-Reynolds number wall turbulence. Annu. Rev. Fluid Mech. 43, 353375.CrossRefGoogle Scholar
Stevens, R. J. A. M., Wilczek, M. & Meneveau, C. 2014 Large-eddy simulation study of the logarithmic law for second- and higher-order moments in turbulent wall-bounded flow. J. Fluid Mech. 757, 888907.Google Scholar
Taylor, G. I. 1938 The spectrum of turbulence. Proc. R. Soc. Lond. A 164, 476490.Google Scholar
Tennekes, H. 1975 Eulerian and Lagrangian time microscales in isotropic turbulence. J. Fluid Mech. 67, 561567.Google Scholar
Tomkins, C. D. & Adrian, R. J. 2005 Energetic spanwise modes in the logarithmic layer of a turbulent boundary layer. J. Fluid Mech. 545, 141162.Google Scholar
Wallace, J. M. 2014 Space–time correlations in turbulent flow: a review. Theor. Appl. Mech. Lett. 4 (2), 22003.CrossRefGoogle Scholar
Wilczek, M. & Narita, Y. 2012 Wave-number–frequency spectrum for turbulence from a random sweeping hypothesis with mean flow. Phys. Rev. E 86, 066308.Google Scholar
Wilczek, M., Stevens, R. J. A. M. & Meneveau, C.2015 Height-dependence of spatio-temporal spectra of wall-bounded turbulence – LES results and model predictions. J. Turbul. (submitted).CrossRefGoogle Scholar
Wilczek, M., Stevens, R. J. A. M., Narita, Y. & Meneveau, C. 2014 A wavenumber–frequency spectral model for atmospheric boundary layers. J. Phys.: Conf. Ser. 524 (1), 012104.Google Scholar
Wills, J. A. B. 1964 On convection velocities in turbulent shear flows. J. Fluid Mech. 20, 417432.Google Scholar
Wyngaard, J. C. 2010 Turbulence in the Atmosphere. Cambridge University Press.Google Scholar
Wyngaard, J. C. & Clifford, S. F. 1977 Taylor’s hypothesis and high-frequency turbulence spectra. J. Atmos. Sci. 34 (6), 922929.Google Scholar
Zhao, X. & He, G.-W. 2009 Space–time correlations of fluctuating velocities in turbulent shear flows. Phys. Rev. E 79, 046316.Google ScholarPubMed