Geometric properties of particle trajectories in turbulent flows

TL;DR

This paper investigates the geometry of particle trajectories in turbulent flows using numerical simulations, focusing on small-scale structures and their statistical properties, revealing the dominance of vortex filaments and helical trajectories in intense events.

Contribution

The study introduces a velocity amplitude cutoff to isolate small-scale turbulent features and demonstrates that their curvature statistics align with a multifractal model, providing new insights into flow geometry.

Findings

High curvature events are linked to flow reversals with low velocity.

Conditioned curvature distribution matches multifractal predictions.

Intense events are primarily associated with helical trajectories.

Abstract

We analyze data from direct numerical simulations of homogeneous and isotropic turbulence (at Re_\lambda \approx 280) and study the statistics of curvature and torsion of Lagrangian trajectories in order to extract informations on the geometry of small scale coherent structures in turbulent flows. We find that, as previously observed by Braun et al and by Xu et al, the high curvature statistics is dominated by large scale flow reversals where the velocity magnitude assumes very low values. In order to focus on small-scales signatures, we introduce a cutoff on the velocity amplitude and we study the probability distribution of curvature conditioned only on those events when the local velocity is not that small. In this way we are able to select small-scales turbulent features, connected to vortex filaments. We show that the conditioned curvature probability density is well reproduced by a multifractal formalism, following previous calculations made for acceleration. Finally, by studying the joint statistics of curvature and torsion we find further evidences that intense events are dominated by helical type trajectories.