On Lagrangian Intermittency from 4-D Particle Tracking Velocimetry measurements of a turbulent von K\'{a}rm\'{a}n flow

TL;DR

This study uses high-density 4D Particle Tracking Velocimetry to analyze turbulence intermittency and irreversibility, revealing that long trajectories exhibit reduced intermittency and align with a multifractal model, differing from Eulerian measurements.

Contribution

The paper introduces methods to remove noise from 4D PTV data and demonstrates that long trajectories show reduced intermittency, aligning with a multifractal model with a smaller intermittency parameter.

Findings

Noise significantly affects short trajectories at small scales.

Long trajectories exhibit reduced intermittency compared to all flow areas.

Scaling laws match a multifractal model with a smaller intermittency parameter.

Abstract

We investigate the ability of 4D Particle Tracking Velocimetry measurements at high particle density to explore intermittency and irreversibility in a turbulent swirling flow at various Reynolds numbers. For this, we devise suitable tools to remove the experimental noise, and compute the statistics of both Lagrangian velocity increments and wavelet coefficients of the Lagrangian power (the time derivative of the kinetic energy along a trajectory). We show that the signature of noise is strongest on short trajectories, and results in deviations from the regularity condition at small time scales. Considering only long trajectories to get rid of such effect, we obtain scaling regimes that are compatible with a reduced intermittency, meaning that long trajectories are also associated with areas of larger regularity. The scaling laws, both in time and Reynolds number, can be described by the multifractal model, with a log-normal spectrum and an intermittency parameter that is three times smaller than in the Eulerian case, where all the areas of the flow are taken into account.