Dispersion Of Inertial Particles Research Articles

Clustering and chaotic motion of heavy inertial particles in an isolated non-axisymmetric vortex

We investigate the dynamics of heavy inertial particles in a flow field due to an isolated, non-axisymmetric vortex. For our study, we consider a canonical elliptical vortex – the Kirchhoff vortex and its strained variant, the Kida vortex. Contrary to the anticipated centrifugal dispersion of inertial particles, which is typical in open vortical flows, we observe the clustering of particles around co-rotating attractors near the Kirchhoff vortex due to its non-axisymmetric nature. We analyse the inertia-modified stability characteristics of the fixed points, highlighting how some of the fixed points migrate in physical space, collide and then annihilate with increasing particle inertia. The introduction of external straining, the Kida vortex being an example, introduces chaotic tracer transport. Using a Melnikov analysis, we show that particle inertia and external straining can compete, where chaotic transport can be suppressed beyond a critical value of particle inertia.

Dispersion of inertial particles in turbulent canopy flows with buoyant and nonbuoyant plumes

Dispersion of inertial particles in turbulent canopy flows with buoyant and nonbuoyant plumes

Dynamics and dispersion of inertial particles in circular cylinder wake flows: A two-way coupled Eulerian–Lagrangian approach

In this paper, the motion of inertial particles in three-dimensional (3D) unsteady cylindrical wake flow is investigated by a two-way coupled Eulerian–Lagrangian approach. At different flow Reynolds numbers (Re), the corresponding striking dynamic property and dispersion mechanism of four particle classes have been studied, with inertia parameterized by means of Stokes number (Sk). It is found that inertial particles with lower Stokes number are expelled from vortex cores, and coherent voids encompass the local Kármán vortex cells. As Stokes number increases, a low velocity particle channel could be formed, which almost coincides with the results in the literature. Moreover, with the increase of Reynolds number, numerous irregular coherent voids are observed in the cylinder wake, and the high-speed particles follow the fluid flow closely when they are contained in the vortices. Although the centrifugal force of Kármán vortex cells significantly affects the dynamics of inertial particles, the fluid flow modulation is believed to be responsible for the distinctive particle dispersion patterns in the vortex streets. For particles with medium inertia, the two-way coupled modulation weakens the centrifugal effect of vortex structures on the particles. This trend declines with the increase of Reynolds number, and vanishes with light particles, while both two-way coupled modulation and the centrifugal effect of vortex structures are almost equally effective with heavy particles. The investigations contribute to a better understanding of the particle-laden flows in practical applications, which will benefit the optimized design of certain machinery and equipment for the industry.

Pair dispersion of inertial particles crossing stably stratified turbulent/non-turbulent interfaces

In the present work we study the pair dispersion of inertial particles crossing two-layer stably stratified turbulent/non-turbulent interfaces (STNTI) of finite thickness. The goal is to investigate two-particle dispersion, a problem of great importance in a wide range of applications, within very complex flows. STNTI flows are in fact constituted not only by non-homogeneous turbulence and density stratification, but also by effects generated by their interaction, such as internal waves. The problem is addressed numerically, two direct numerical simulations (DNS) of STNTI flow are performed: moderate intensity turbulence, Reλ=35,44 is produced in the DNSs using a convective forcing (heat source at the domain boundary) in one case, and a forcing that mimics a vertically oscillating grid, through an immersed boundary method, in the other. We tracked inertial particles, with one-way coupling approach, using a modified Basset-Boussinesq-Oseen equation which includes a stratification-induced term FS. We first analyze particle pairs settling from the turbulent region toward the STNTI and then pairs moving in the opposite direction from the non-turbulent layer. In general results for all pair tested present a t2-scaling law for small times. Pairs crossing the STNTI from the non-turbulent region show a peculiar dispersion behavior: approaching the internal waves layer, pairs tend to reduce the dispersion rate and then they present a fast growing separation regime (tα, α≥6) possibly induced by non-local effects. Different particle densities, which affect inertia, the initial pair separation and the effect of stratification on the dispersion, by means of FS, are tested as well. Results confirm the importance of the first two parameters for the pair evolution. The stratification force, which has been not taken into account so far, plays as well an important role, which may lead to discrepancies in pair dispersion larger than the role FS has in the equation of motion itself.

Enhanced settling and dispersion of inertial particles in surface waves

Particulate matter in the environment, such as sediment, marine debris and plankton, is transported by surface waves. The transport of these inertial particles is different from that of fluid parcels described by Stokes drift. In this study, we consider the transport of negatively buoyant particles that settle in flow induced by surface waves as described by linear wave theory in arbitrary depth. We consider particles that fall under both a linear drag regime in the low Reynolds number limit and in a nonlinear drag regime in the transitional Reynolds number range. Based on an analysis of typical applications, we find that the nonlinear regime is the most widely applicable. From an expansion in the particle Stokes number, we find kinematic expressions for inertial particle motion in waves, and from a multiscale expansion in the dimensionless wave amplitude, we find expressions for the wave-averaged drift velocities. These drift velocities are analogous to Stokes drift and can be used in large-scale models that do not resolve surface waves. We find that the horizontal drift velocity is reduced relative to the Stokes drift of fluid parcels and that the vertical drift velocity is enhanced relative to the particle terminal settling velocity. We also demonstrate that a cloud of settling particles released simultaneously will disperse in the horizontal direction. Finally, we discuss the accuracy of our expressions by comparing against numerical simulations, which show excellent agreement, and against experimental data, which show the same trends.

Dispersion of inertial particles in cellular flows in the small-Stokes, large-Péclet regime

Abstract

Large eddy simulation of inertial particles dispersion in a turbulent gas-particle channel flow bounded by rough walls

The purpose of this paper is to understand the capability and consistency of large eddy simulation (LES) in Eulerian–Lagrangian studies aimed at predicting inertial particle dispersion in turbulent wall-bounded flows, in the absence of ad hoc closure models in the Lagrangian equations of particle motion. The degree of improvement granted by LES models is object of debate, in terms of both accurate prediction of particle accumulation and local particle segregation; therefore, we assessed the accuracy in the prediction of the particle velocity statistics by comparison against direct numerical simulation (DNS) of a finer computational mesh, under both one-way and two-way coupling regimes. We performed DNS and LES at friction Reynolds number $$\hbox {Re}_{\tau }=180$$ in smooth and rough channels, tracking particles with different inertia, with the aim to conduct a parametric study that examines the accuracy of particle statistics obtained from LES computations. The issue has been widely analysed in turbulent flow bounded by smooth walls, whereas the effect of rough boundaries on momentum coupled two-phase flows has been much less investigated until now. The action of the roughness of the wall is studied in terms of both turbulence modification and particle interaction with the wall surface due to particle rebounding off the solid boundary, without the introduction of a virtual rebound model. Results show that resolved LES adequately predicts particle-induced changes in both fluid and particle statistics in rough channels, at least for the range of parameters considered here.

Irreversibility inversions in two-dimensional turbulence

In this paper we consider a recent theoretical prediction (Bragg \emph{et al.}, Phys. Fluids \textbf{28}, 013305 (2016)) that for inertial particles in 2D turbulence, the nature of the irreversibility of the particle-pair dispersion inverts when the particle inertia exceeds a certain value. In particular, when the particle Stokes number, ${\rm St}$, is below a certain value, the forward-in-time (FIT) dispersion should be faster than the backward-in-time (BIT) dispersion, but for ${\rm St}$ above this value, this should invert so that BIT becomes faster than FIT dispersion. This non-trivial behavior arises because of the competition between two physically distinct irreversibility mechanisms that operate in different regimes of ${\rm St}$. In 3D turbulence, both mechanisms act to produce faster BIT than FIT dispersion, but in 2D turbulence, the two mechanisms have opposite effects because of the flux of energy from the small to the large scales. We supplement the qualitative argument given by Bragg \emph{et al.} (Phys. Fluids \textbf{28}, 013305 (2016)) by deriving quantitative predictions of this effect in the short time limit. We confirm the theoretical predictions using results of inertial particle dispersion in a direct numerical simulation of 2D turbulence. A more general finding of this analysis is that in turbulent flows with an inverse energy flux, inertial particles may yet exhibit a net downscale flux of kinetic energy because of their non-local in-time dynamics.

Developments and difficulties in predicting the relative velocities of inertial particles at the small-scales of turbulence

In this paper, we consider the development of theoretical models to predict the relative velocities of inertial particles in isotropic turbulence. In particular, we use our recently developed theory for the backward-in-time (BIT) relative dispersion of inertial particles in turbulence [Bragg et al., “Forward and backward in time dispersion of fluid and inertial particles in isotropic turbulence,” Phys. Fluids 28, 013305 (2016)] to develop the theoretical model by Pan and Padoan [“Relative velocity of inertial particles in turbulent flows,” J. Fluid Mech. 661, 73 (2010)]. We focus on the most difficult regime to model, the dissipation range, and find that the modified Pan and Padoan model (that uses the BIT dispersion theory) can lead to significantly improved predictions for the relative velocities, when compared with the Direct Numerical Simulation (DNS) data. However, when the particle separation distance, r, is less than the Kolmogorov lengthscale, η, the modified model overpredicts the DNS data. We explain how these overpredictions arise from two assumptions in the BIT dispersion theory that are in general not satisfied when the final separation of the BIT dispersing particles is <η. We then demonstrate the failure of both the original and modified versions of the Pan and Padoan model to predict the correct scale-invariant forms for the inertial particle relative velocity structure functions in the dissipation regime. It is shown how this failure, which is also present in other models, is associated with our current inability to correctly predict not only the quantitative but also the qualitative behavior of the radial distribution function in the dissipation range when St=O(1).

Single-particle Lagrangian and structure statistics in kinematically simulated particle-laden turbulent flows

Kinematic simulation (KS) is a means of generating a turbulent-like velocity field, in a manner that enforces a desired input Eulerian energy spectrum. Such models have also been applied in particle-laden flows, due to their ability to enforce spatial organization of the fluid velocity field when simulating the trajectories of individual Lagrangian particles. A critical evaluation of KS is presented; in particular, we examine its ability to reproduce single-particle Lagrangian statistics. Also the ability of KS to reproduce the preferential concentration of inertial particles is examined. Some computational results are presented, in which particles are transported alternatively by (1) turbulence generated by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations, and (2) KS. The effect of unsteadiness formulation in particular is examined. We find that even steady KS qualitatively reproduces the continuity effect, clustering of inertial particles, the elevated dispersion of inertial particles over fluid particles, and the intermittency of Lagrangian velocity signals, but generally not to the same extent as is seen in the DNS.

Forward and backward in time dispersion of fluid and inertial particles in isotropic turbulence

In this paper, we investigate both theoretically and numerically the Forward-In-Time (FIT) and Backward-In-Time (BIT) dispersion of fluid and inertial particle-pairs in isotropic turbulence. Fluid particles are known to separate faster BIT than FIT in three-dimensional turbulence, and we find that inertial particles do the same. However, we find that the irreversibility in the inertial particle dispersion is in general much stronger than that for fluid particles. For example, the ratio of the BIT to FIT mean-square separation can be up to an order of magnitude larger for the inertial particles than for the fluid particles. We also find that for both the inertial and fluid particles, the irreversibility becomes stronger as the scale of their separation decreases. Regarding the physical mechanism for the irreversibility, we argue that whereas the irreversibility of fluid particle-pair dispersion can be understood in terms of a directional bias arising from the energy transfer process in turbulence, inertial particles experience an additional source of irreversibility arising from the non-local contribution to their velocity dynamics, a contribution that vanishes in the limit St → 0, where St is the particle Stokes number. For each given initial (final, in the BIT case) separation, r0, there is an optimum value of St for which the dispersion irreversibility is strongest, as such particles are optimally affected by both sources of irreversibility. We derive analytical expressions for the BIT, mean-square separation of inertial particles and compare the predictions with numerical data obtained from a Reλ ≈ 582 (where Reλ is the Taylor Reynolds number) Direct Numerical Simulation (DNS) of particle-laden isotropic turbulent flow. The small-time theory, which in the dissipation range is valid for times ≤max[Stτη, τη] (where τη is the Kolmogorov time scale), is in excellent agreement with the DNS. The theory for long-times is in good agreement with the DNS provided that St is small enough so that the inertial particle motion at long-times may be considered as a perturbation about the fluid particle motion, a condition that would in fact be satisfied for arbitrary St at sufficiently long-times in the limit Reλ → ∞.

Particle subgrid scale modelling in large-eddy simulations of particle-laden turbulence

This study is concerned with particle subgrid scale (SGS) modelling in large-eddy simulations (LESs) of particle-laden turbulence. Although many particle-laden LES studies have neglected the effect of the SGS on the particles, several particle SGS models have been proposed in the literature. In this research, the approximate deconvolution method (ADM) and the stochastic models of Fukagata et al. (Dynamics of Brownian particles in a turbulent channel flow, Heat Mass Transf. 40 (2004), 715–726) Shotorban and Mashayek (A stochastic model for particle motion in large-eddy simulation, J. Turbul. 7 (2006), 1–13) and Berrouk et al. (Stochastic modelling of inertial particle dispersion by subgrid motion for LES of high Reynolds number pipe flow, J. Turbul. 8 (2007), pp. 1–20) are analysed. The particle SGS models are assessed using both a priori and a posteriori simulations of inertial particles in a periodic box of decaying, homogeneous and isotropic turbulence with an initial Reynolds number of Reλ = 74. The model results are compared with particle statistics from a direct numerical simulation (DNS). Particles with a large range of Stokes numbers are tested using various filter sizes and stochastic model constant values. Simulations with and without gravity are performed to evaluate the ability of the models to account for the crossing trajectory and continuity effects. The results show that ADM improves results but is only capable of recovering a portion of the SGS turbulent kinetic energy. Conversely, the stochastic models are able to recover sufficient SGS energy, but show a large range of results dependent on the Stokes number and filter size. The stochastic models generally perform best at small Stokes numbers, but are unable to predict preferential concentration.

Prediction of a particle-laden turbulent channel flow: Examination of two classes of stochastic dispersion models

Nowadays, two families of stochastic models are mainly used to predict the dispersion of inertial particles in inhomogeneous turbulent flows. This first one is named “normalized model” and the second one “Generalized Langevin Model (GLM)”. Nevertheless, the main differences between the normalized and GLM models have not been thoroughly investigated. Is there a model which is more suitable to predict the particle dispersion in inhomogeneous turbulence? We propose in the present study to clarify this point by computing a particle-laden turbulent channel flow using a GLM-type model, and also a normalized-type model. Particle statistics (such as concentration, mean and rms particle velocity, fluid-particle velocity covariances) will be provided and compared to Direct Numerical Simulation (DNS) data in order to assess the performance of both dispersion models. It will be shown that the normalized dispersion model studied can predict correctly the effect of particle inertia on some dispersion statistics, but not on all. For instance, it was found that the prediction of the particle kinetic shear stress and some components of the fluid-particle covariance is not physically acceptable.

Transports of air particulate matters in the atmospheric boundary layer-numerical studies using Eulerian and Lagrangian methods

Transports of air particulate matters (PM) from face sources in the atmospheric boundary layer (ABL) are investigated by the Eulerian single fluid model and the Lagrangian trajectory method, respectively. Large eddy simulation is used to simulate the fluid phase for high accuracy in both two approaches. The mean and fluctuating PM concentrations, as well as instantaneous PM distributions at different downstream and height positions, are presented. Higher mean and fluctuating particle concentrations are predicted by the Eulerian approach than the Lagrangian one. For the Lagrangian method, PM distributions cluster near the ground-wall because of the preferential dispersion of inertial particles by turbulence structures in the ABL, while it cannot be obtained by the Eulerian single fluid method, because the two-phase velocity differences are neglected in the Eulerian method.

Large Eddy Simulation of Inertial Particle Preferential Dispersion in a Turbulent Flow over a Backward-Facing Step

Large eddy simulation of inertial particle dispersion in a turbulent flow over a backward-facing step was performed. The numerical results of both instantaneous particle dispersion and two-phase velocity statistics were in good agreement with the experimental measurements. The analysis of preferential dispersion of inertial particles was then presented by a wavelets analysis method for decomposing the two-phase turbulence signal obtained by numerical simulations, showing that the inertial particle concentration is separation from the Gaussian random distribution with very strong intermittencies. The statistical PDF of vorticity seen by particles shows that the inertial particles tend to accumulate in low vorticity regions where ∇u: ∇u is larger than zero. The concentration distribution of particle preferential dispersion preserves the historical effects. The research conclusions are useful for further understanding the two-phase turbulence physics and establishing accurate engineering prediction models of particle dispersion.

Anisotropy in pair dispersion of inertial particles in turbulent channel flow

The rate at which two particles separate in turbulent flows is of central importance to predict the inhomogeneities of particle spatial distribution and to characterize mixing. Pair separation is analyzed for the specific case of small, inertial particles in turbulent channel flow to examine the role of mean shear and small-scale turbulent velocity fluctuations. To this aim an Eulerian-Lagrangian approach based on pseudo-spectral direct numerical simulation (DNS) of fully developed gas-solid flow at shear Reynolds number Reτ = 150 is used. Pair separation statistics have been computed for particles with different inertia (and for inertialess tracers) released from different regions of the channel. Results confirm that shear-induced effects predominate when the pair separation distance becomes comparable to the largest scale of the flow. Results also reveal the fundamental role played by particles-turbulence interaction at the small scales in triggering separation during the initial stages of pair dispersion. These findings are discussed examining Lagrangian observables, including the mean square separation, which provide prima facie evidence that pair dispersion in non-homogeneous anisotropic turbulence has a superdiffusive nature and may generate non-Gaussian number density distributions of both particles and tracers. These features appear to persist even when the effects of shear dispersion are filtered out, and exhibit strong dependency on particle inertia. Application of present results is discussed in the context of modelling approaches for particle dispersion in wall-bounded turbulent flows.

Two-phase micro- and macro-time scales in particle-laden turbulent channel flows

The micro- and macro-time scales in two-phase turbulent channel flows are investigated using the direct numerical simulation and the Lagrangian particle trajectory methods for the fluid- and the particle-phases, respectively. Lagrangian and Eulerian time scales of both phases are calculated using velocity correlation functions. Due to flow anisotropy, micro-time scales are not the same with the theoretical estimations in large Reynolds number (isotropic) turbulence. Lagrangian macro-time scales of particle-phase and of fluid-phase seen by particles are both dependent on particle Stokes number. The fluid-phase Lagrangian integral time scales increase with distance from the wall, longer than those time scales seen by particles. The Eulerian integral macro-time scales increase in near-wall regions but decrease in out-layer regions. The moving Eulerian time scales are also investigated and compared with Lagrangian integral time scales, and in good agreement with previous measurements and numerical predictions. For the fluid particles the micro Eulerian time scales are longer than the Lagrangian ones in the near wall regions, while away from the walls the micro Lagrangian time scales are longer. The Lagrangian integral time scales are longer than the Eulerian ones. The results are useful for further understanding two-phase flow physics and especially for constructing accurate prediction models of inertial particle dispersion.

Two-way coupled stochastic model for dispersion of inertial particles in turbulence

AbstractTurbulent two-phase flows are characterized by the presence of multiple time and length scales. Of particular interest in flows with non-negligible interphase momentum coupling are the time scales associated with interphase turbulent kinetic energy transfer (TKE) and inertial particle dispersion. Point-particle direct numerical simulations (DNS) of homogeneous turbulent flows laden with sub-Kolmogorov size particles report that the time scale associated with the interphase TKE transfer behaves differently with Stokes number than the time scale associated with particle dispersion. Here, the Stokes number is defined as the ratio of the particle momentum response time scale to the Kolmogorov time scale of turbulence. In this study, we propose a two-way coupled stochastic model (CSM), which is a system of two coupled Langevin equations for the fluctuating velocities in each phase. The basis for the model is the Eulerian–Eulerian probability density function formalism for two-phase flows that was established in Pai & Subramaniam (J. Fluid Mech., vol. 628, 2009, pp. 181–228). This new model possesses the unique capability ofsimultaneouslycapturing the disparate dependence of the time scales associated with interphase TKE transfer and particle dispersion on Stokes number. This is ascertained by comparing predicted trends of statistics of turbulent kinetic energy and particle dispersion in both phases from CSM, for varying Stokes number and mass loading, with point-particle DNS datasets of homogeneous particle-laden flows.

Intrinsic filtering errors of Lagrangian particle tracking in LES flow fields

Large-eddy simulation (LES) of two-phase turbulent flows exhibits quantitative differences in particle statistics if compared to direct numerical simulation (DNS) which, in the context of the present study, is considered the exact reference case. Differences are primarily due to filtering, a fundamental intrinsic feature of LES. Filtering the fluid velocity field yields approximate computation of the forces acting on particles and, in turn, trajectories that are inaccurate when compared to those of DNS. In this paper, we focus precisely on the filtering error for which we quantify a lower bound. To this aim, we use a DNS database of inertial particle dispersion in turbulent channel flow and we perform a priori tests in which the error purely due to filtering is singled out removing error accumulation effects, which would otherwise lead to progressive divergence between DNS and LES particle trajectories. By applying filters of different type and width at varying particle inertia, we characterize the statistical properties of the filtering error as a function of the wall distance. Results show that filtering error is stochastic and has a non-Gaussian distribution. In addition, the distribution of the filtering error depends strongly on the wall-normal coordinate being maximum in the buffer region. Our findings provide insight on the effect of sub-grid scale velocity field on the force driving the particles, and establish the requirements that any closure model aimed at recovering sub-grid scale effects on the dynamics of inertial particles must satisfy.

Inertial-particle dispersion and diffusion

We analytically investigate the dynamics of inertial particles in incompressible flows in the limit of small but finite inertia, focusing on two specific instances. First, we study the concentration of particles continuously emitted from a point source with a given exit velocity distribution. The anisotropy of the latter turns out to be a necessary factor for the presence of a correction (with respect to the corresponding tracer case) at order square root of the Stokes number. Secondly, by means of a multiple-scale expansion, we analyse the particle effective diffusivity, and in particular its dependence on Brownian diffusivity, gravity effects and particle-to-fluid density ratio. In both cases, we obtain forced advection–diffusion equations for auxiliary quantities in the physical space, thus simplifying the problem from the full phase space to a system which can easily be solved numerically.