Particle‐level dynamics of clusters: Experiments in a gas‐fluidized bed

Abstract

Clustering is critical to understanding the multiscale behavior of fluidization. However, its time-resolved evolution at the particle level is seldom touched. Here, we explore both the time-averaged and time-resolved dynamics of clusters in a quasi-2D fluidized bed. Particle tracking velocimetry is adopted, and then clusters are identified by using Voronoi analysis. The time-averaged results show that the number distribution of the cluster size follows a power law ($ n À2:2 c ) except for large clusters (n c > 100). Time-resolved analysis demonstrates that the cluster coalescence can be simplified as a collision between two inelastic clusters, during which the mean speed, kinetic energy, and total Voronoi cell area of particles decrease until the formation of the big cluster. Then, a model is proposed to predict its energy loss, which gives ΔE ~t3/2 . Moreover, the breakup of the cluster is linked to increasing dimensionless torque on the particles.

Particle tracking velocimetry study of the nonequilibrium characteristics of a fluidized bed

“…Additionally, Gaussian fitting curves are plotted for comparison. Similar nonequilibrium velocity distributions have been reported in the literature 44,48,54,55,71 . By contrast, VPDDs corresponding to the dense zones also show non‐Gaussian behavior including the skewed or bimodal distributions, in certain areas (e.g., cells B2–B4, E3, and E4).…”

“…Similar nonequilibrium velocity distributions have been reported in the literature. 44,48,54,55,71 By contrast, VPDDs corresponding to the dense zones also show non-Gaussian behavior including the skewed or bimodal distributions, in certain areas (e.g., cells B2-B4, E3, and E4). This is contrast with knowledge that the VPDD in the dense emulsion is nearly Gaussian at equilibrium, 44,48 suggesting the need for the further investigation of the underlying granular structure in the fluidized bed.…”

“…[2][3][4]27,[30][31][32][33][34][35][36] The onset of flow instability in a fluidized bed, 37 which is associated with a transition from the particulate/homogeneous fluidization to aggregative/heterogeneous fluidization, and the determination of the minimum bubbling velocity (especially for fine particles belonging to Geldart group A), is notoriously hard to predict. 36,38,39 With changes in operating conditions (e.g., the gas velocity and solids flux) and material properties, the flow instability further develops such that a homogeneous suspension gives way to various heterogeneous, mesoscale structures in terms of, for example, bubbles, clusters, and vortices, 2,8,[40][41][42][43][44][45][46][47] where Kn ε and Kn T could be large besides large Kn U . 8 For example, in a moderately dense fluidization regime, say that of a bubbling fluidized bed, the flow is dominated by binary particle collisions where the particle velocity distribution is nearly Maxwellian in both the dense emulsion and dilute bubbles but is much skewed from the Maxwellian distribution and is even bimodal over the interface of bubbles/ clusters.…”

Characterizing Particle Clustering Behavior in Dense Gas–Solid Suspensions

“…The Voronoi ̈diagram method is popular in analyzing gas−solid flow. 23,[26][27][28]34,46 For a set of particles distributed in a three-dimensional domain, with the help of the Voronoi ̈diagram method, the local solid concentration inside a Voronoi ̈volume is calculated as ε s = V p /V vor , where V p and V vor are, respectively, the volume of particle and that of the corresponding Voronoi ̈volume. Monchaux et al 23 and Baker et al 26 proposed a method to identify particle cluster based on the Voronoi ̈diagram approach.…”

“…Recently, the Voronoi ̈tessellation-based method has received more and more attention in characterizing heterogeneous flow structures, as no length scale needs to be chosen a priori in this method and the local concentration field is naturally defined. Monchaux et al, 23 Tagawa et al, 24 Dejoan and Monchaux, 25 Baker et al 26 and Wang et al 27 where I a = 2/5m a R a 2 torque of a particle: T a = ∑ b∈contact list (R a n ab × F ba,t ) normal contact force between two particles:…”