Network analysis of Reynolds number scaling in wall-bounded Lagrangian mixing

TL;DR

This study uses network analysis of particle trajectories from direct numerical simulations to understand how Reynolds number influences wall-normal mixing and dispersion in turbulent channel flows, revealing Reynolds-independent network properties at higher Re.

Contribution

It introduces a novel network-based framework to analyze particle dispersion in turbulent flows across different Reynolds numbers, highlighting mechanisms and scaling behaviors.

Findings

Dispersion depends on initial position and time elapsed.

Two mechanisms inhibit near-wall dispersion, varying with Re.

Network properties become Reynolds-independent at higher Re when scaled appropriately.

Abstract

The dispersion and mixing of passive particles in a turbulent channel flow is investigated by means of a network-based representation of their motion. We employ direct numerical simulations at five different Reynolds numbers, from $\mathrm{Re}_{\tau}$ = 180 up to $\mathrm{Re}_{\tau}$ = 950, and obtain sets of particle trajectories via numerical integration. By dividing the channel domain into wall-normal levels, the motion of particles across these levels is used to build a time-varying complex network, which is able to capture the transient phase of the wall-normal mixing process and its dependence on the Reynolds number, $\mathrm{Re}_{\tau}$. Using network metrics, we observe that the dispersion of clouds of tracers depends highly on both their wall-normal starting position and the time elapsed from their release. We identify two main mechanisms that contribute to the long lasting inhibition of the dispersion of particles released near the walls. We also show how the relative importance of these mechanisms varies with the Reynolds number. In particular, at low $\mathrm{Re}_{\tau}$ the weaker velocity fluctuations appear dominant in inhibiting dispersion, while at higher Reynolds numbers a larger role is played by cyclic patterns of motion. At the higher Reynolds numbers employed in this work, we find that most network properties are Reynolds-independent when scaled with outer flow variables. Instead, at lower $\mathrm{Re}_{\tau}$, the aforementioned scaling is not observed. We explore the meaning of the emergence of this scaling in relation to the features of dispersion and to the network definition.