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Comprehensive multifractal analysis of turbulent velocity using the wavelet leaders

Author

Listed:
  • B. Lashermes
  • S. G. Roux
  • P. Abry
  • S. Jaffard

Abstract

The multifractal framework relates the scaling properties of turbulence to its local regularity properties through a statistical description as a collection of local singularities. The multifractal properties are moreover linked to the multiplicative cascade process that creates the peculiar properties of turbulence such as intermittency. A comprehensive estimation of the multifractal properties of turbulence from data analysis, using a tool valid for all kind of singularities (including oscillating singularities) and mathematically well-founded, is thus of first importance in order to extract a reliable information on the underlying physical processes. The wavelet leaders yield a new multifractal formalism which meets all these requests. This paper aims at describing it and at applying it to experimental turbulent velocity data. After a detailed discussion of the practical use of the wavelet leader based multifractal formalism, the following questions are carefully investigated: (1) What is the dependence of multifractal properties on the Reynolds number? (2) Are oscillating singularities present in turbulent velocity data? (3) Which multifractal model does correctly account for the observed multifractal properties? Results from several data set analysis are used to discuss the dependence of the computed multifractal properties on the Reynolds number but also to assess their common or universal component. An exact though partial answer (no oscillating singularities are detected) to the issue of the presence of oscillating singularities is provided for the first time. Eventually an accurate parameterization with cumulant exponents up to order 4 confirms that the log-normal model (with c 2 =-0.025±0.002) correctly accounts for the universal multifractal properties of turbulent velocity. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2008

Suggested Citation

  • B. Lashermes & S. G. Roux & P. Abry & S. Jaffard, 2008. "Comprehensive multifractal analysis of turbulent velocity using the wavelet leaders," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 61(2), pages 201-215, January.
  • Handle: RePEc:spr:eurphb:v:61:y:2008:i:2:p:201-215
    DOI: 10.1140/epjb/e2008-00058-4
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    Citations

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    Cited by:

    1. Xiong, Gang & Zhang, Shuning & Yang, Xiaoniu, 2012. "The fractal energy measurement and the singularity energy spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6347-6361.
    2. Makowiec, Danuta & Dudkowska, Aleksandra & Gała̧ska, Rafał & Rynkiewicz, Andrzej, 2009. "Multifractal estimates of monofractality in RR-heart series in power spectrum ranges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3486-3502.
    3. Xiong, Gang & Zhang, Shuning & Liu, Qiang, 2012. "The time-singularity multifractal spectrum distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4727-4739.
    4. Serrano, E. & Figliola, A., 2009. "Wavelet Leaders: A new method to estimate the multifractal singularity spectra," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2793-2805.
    5. Calif, Rudy & Schmitt, François G. & Huang, Yongxiang, 2013. "Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4106-4120.
    6. Xiong, Gang & Yu, Wenxian & Zhang, Shuning, 2015. "Singularity power spectrum distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 431(C), pages 63-73.
    7. Welter, Guilherme Sausen & Wittwer, Adrián Roberto & Degrazia, Gervásio Annes & Acevedo, Otávio Costa & de Moraes, Osvaldo Luiz Leal & Anfossi, Domenico, 2009. "Measurements of the Kolmogorov constant from laboratory and geophysical wind data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3745-3751.
    8. Ayache, Antoine & Esser, Céline & Kleyntssens, Thomas, 2019. "Different possible behaviors of wavelet leaders of the Brownian motion," Statistics & Probability Letters, Elsevier, vol. 150(C), pages 54-60.
    9. Monjoly, Stéphanie & André, Maïna & Calif, Rudy & Soubdhan, Ted, 2017. "Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach," Energy, Elsevier, vol. 119(C), pages 288-298.

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