Experiments in Fluids

, 56:188 | Cite as

Wavelet analysis of wall turbulence to study large-scale modulation of small scales

  • W. J. BaarsEmail author
  • K. M. Talluru
  • N. Hutchins
  • I. Marusic
Research Article


Wavelet analysis is employed to examine amplitude and frequency modulations in broadband signals. Of particular interest are the streamwise velocity fluctuations encountered in wall-bounded turbulent flows. Recent studies have shown that an important feature of the near-wall dynamics is the modulation of small scales by large-scale motions. Small- and large-scale components of the velocity time series are constructed by employing a spectral separation scale. Wavelet analysis of the small-scale component decomposes the energy in joint time–frequency space. The concept is to construct a low-dimensional representation of the small-scale time-varying spectrum via two new time series: the instantaneous amplitude of the small-scale energy and the instantaneous frequency. Having the latter in a time-continuous representation allows a more thorough analysis of frequency modulation. By correlating the large-scale velocity with the concurrent small-scale amplitude and frequency realizations, both amplitude and frequency modulations are studied. In addition, conditional averages of the small-scale amplitude and frequency realizations depict unique features of the scale interaction. For both modulation phenomena, the much studied time shifts, associated with peak correlations between the large-scale velocity and small-scale amplitude and frequency traces, are addressed. We confirm that the small-scale amplitude signal leads the large-scale fluctuation close to the wall. It is revealed that the time shift in frequency modulation is smaller than that in amplitude modulation. The current findings are described in the context of a conceptual mechanism of the near-wall modulation phenomena.


Amplitude Modulation Time Shift Instantaneous Frequency Wavelet Power Spectrum Streamwise Velocity Fluctuation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to gratefully acknowledge the Australian Research Council for financial support. Furthermore, we would like to give special thanks to Dr. Daniel Chung for insightful discussions.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • W. J. Baars
    • 1
    Email author
  • K. M. Talluru
    • 2
  • N. Hutchins
    • 1
  • I. Marusic
    • 1
  1. 1.Department of Mechanical EngineeringUniversity of MelbourneParkvilleAustralia
  2. 2.Discipline of Mechanical EngineeringUniversity of NewcastleNewcastleAustralia

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