Probing turbulence intermittency via Auto-Regressive Moving-Average models

TL;DR

This paper introduces a novel ARMA-based index to measure turbulence intermittency, providing a more efficient way to analyze intermittency corrections in turbulent flows using shorter time series.

Contribution

The paper proposes a new ARMA-based index, $$, for probing turbulence intermittency, demonstrating its effectiveness with experimental data and its relation to traditional measures.

Findings

$$ is proportional to traditional intermittency correction.

$$ requires shorter time series for analysis.

The index effectively reconstructs spatial intermittency.

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 $\Upsilon$ 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 K\'arm\'an swirling flow, we show that $\Upsilon$ 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 $\Upsilon$ is a suitable index to reconstruct the spatial intermittency of the dissipation in both numerical and experimental turbulent fields.