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arXiv · 1407.5478

Probing turbulence intermittency via Auto-Regressive Moving-Average models

Davide Faranda·Flavio Maria Emanuele Pons·Berengere Dubrulle·Francois Daviaud
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Abstract

We suggest a new approach to probing intermittency corrections to the Kolmogorov law in turbulent flows based on the Auto-Regressive Moving-Average modeling of turbulent time series. We introduce a new index $Υ$ that measures the distance from a Kolmogorov-Obukhov model in the Auto-Regressive Moving-Average models space. Applying our analysis to Particle Image Velocimetry and Laser Doppler Velocimetry measurements in a von Kármán swirling flow, we show that $Υ$ is proportional to the traditional intermittency correction computed from the structure function. Therefore it provides the same information, using much shorter time series. We conclude that $Υ$ is a suitable index to reconstruct the spatial intermittency of the dissipation in both numerical and experimental turbulent fields.

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Davide Faranda, Flavio Maria Emanuele Pons, Berengere Dubrulle, Francois Daviaud. 2014-07-21. Probing turbulence intermittency via Auto-Regressive Moving-Average models. https://doi.org/10.1103/physreve.90.061001

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