A. Cartellier | Searcharxiv

arXiv2022

Buoyancy driven bubbly flows: role of meso-scale structures on the relative motion between phases in bubble columns operated in the heterogeneous regime

The hydrodynamics of bubble columns in the heterogeneous regime is investigated from experiments with bubbles at large particle Reynolds numbers and without coalescence. The void fraction field $\varepsilon$ at small scales, analyzed with Voronoï tessellations, corresponds to a Random Poisson Process (RPP) in homogeneous conditions but it significantly differs from a RPP in the heterogeneous regime. The distance to a RPP allows identifying meso-scale structures, namely clusters, void regions and intermediate regions. A series of arguments demonstrate that the bubble motion is driven by the dynamics of these structures. Notably, bubbles in clusters (respectively in intermediate regions) are moving up faster, up to 3.5 (respectively 2) times the terminal velocity, than bubbles in void regions those absolute velocity equals the mean liquid velocity. Besides, the mean unconditional relative velocity of bubbles is recovered from mean relative velocities conditional to meso-scale structures, weighted by the proportion of bubbles in each structure. Assuming buoyancy-inertia equilibrium for each structure, the relative velocity is related with the characteristic size and concentration of meso-scale structures. By taking the latter quantities values at large gas superficial velocities, a cartoon of the internal flow structure is proposed. Arguments are put forward to help understanding why the relative velocity scales as $(gD\varepsilon)^{1/2}$ (with $D$ the column's diameter and $g$ gravity's acceleration). The proposed cartoon seems consistent with a fast-track mechanism that, for the moderate Rouse numbers studied, leads to liquid velocity fluctuations proportional to the relative velocity. The potential impact of coalescence on the above analysis is also commented.

physics.flu-dyn↗

arXiv2019

Study on preferential concentration of inertial particles in homogeneous isotropic turbulence via Big-Data techniques

We present an experimental study of the preferential concentration of sub-Kolmogorov inertial particles in active-grid-generated homogeneous and isotropic turbulence, characterized via Voronoï tessellations. We show that the detection and quantification of clusters and voids is influenced by the intensity of the laser and high values of particles volume fraction $ϕ_v$. Different biases on the statistics of Voronoï cells are analyzed to improve the reliability of the detection and the robustness in the characterization of clusters and voids. We do this by adapting Big-Data techniques that allow to process the particle images up to 10 times faster than standard algorithms. Finally, as preferential concentration is known to depend on multiple parameters, we performed experiments where one parameter was varied and all others were kept constant ($ϕ_v$, Reynolds number based on the Taylor length scale $Re_λ$, and residence time of the particles interacting with the turbulence). Our results confirm, in agreement with published work, that clustering increases with both $ϕ_v$ and $Re_λ$. On the other hand, we find new evidence that the mean size of clusters increases with $Re_λ$ but decreases with $ϕ_v$ and that the cluster settling velocity is strongly affected by $Re_λ$ up to the maximum value studied here, $Re_λ= 250$.

physics.flu-dyn↗