Probing turbulence intermittency via autoregressive moving-average models

Area of Science:

• Fluid Dynamics
• Turbulence Research
• Statistical Physics

Background:

• The Kolmogorov law describes energy dissipation in turbulent flows.
• Intermittency corrections are necessary to accurately model turbulent phenomena.
• Traditional methods for analyzing intermittency require extensive time series data.

Purpose of the Study:

• To develop a novel approach for probing intermittency corrections in turbulent flows.
• To introduce a new index (Υ) for quantifying deviations from the Kolmogorov-Obukhov model.
• To assess the efficiency of the proposed method compared to existing techniques.

Main Methods:

• Autoregressive moving-average (ARMA) modeling of turbulent time series.
• Introduction and application of the index Υ.
• Analysis of particle image velocimetry (PIV) and laser Doppler velocimetry (LDV) data from a von Kármán swirling flow.

Main Results:

• The index Υ effectively measures the distance from the Kolmogorov-Obukhov model in the ARMA model space.
• Υ is shown to be proportional to traditional intermittency corrections derived from structure functions.
• The proposed method yields the same information as traditional methods but requires significantly shorter time series.

Conclusions:

• The developed ARMA-based approach provides an efficient way to study intermittency in turbulent flows.
• The index Υ is a suitable metric for reconstructing intermittency in experimental turbulent fields.
• This method offers a more data-efficient alternative for analyzing turbulent phenomena.