Can turbophoresis be predicted by large-eddy simulation?

Kuerten, Johannes G.M.; Vreman, A.W.

doi:10.1063/1.1824151

Cited by 110 publications

(98 citation statements)

References 16 publications

(17 reference statements)

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“…It has often been successful provided that a dissipation term is added to control the extra fluctuations introduced by defiltering. Recently, Kuerten and Vreman 14,15 and later Shotorban and Maskayek 17 showed that defiltering of the fluid velocity also yields a useful subgrid model in the particle equation of motion.…”

Section: B Particlesmentioning

confidence: 99%

“…The results depend on the subgrid model applied, but even for an "optimal" subgrid model, a substantial difference between the DNS and LES results remains, especially for particle relaxation times of the same order as the Kolmogorov time. It has also been shown 14,15 that results improve if a defiltered fluid velocity 16 is used in the particle equation of motion, and if an adequate subgrid model, such as the dynamic eddyviscosity model, 6 is applied. Later, a similar deconvolution model was proposed as a subgrid model in the particle equation by Shotorban and Mashayek,17 who applied this procedure in LES of particle-laden homogeneous shear flow.…”

Section: Introductionmentioning

confidence: 99%

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Subgrid modeling in particle-laden channel flow

2006

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Direct numerical simulation ͑DNS͒ and large-eddy simulation ͑LES͒ of particle-laden turbulent channel flow, in which the particles experience a drag force, are investigated for two subgrid models and several Reynolds and Stokes numbers. In this flow, turbophoresis leads to an accumulation of particles near the walls. The objectives of the work are to investigate the accuracy of the subgrid models studied with respect to particle behavior and to explain the observed particle behavior predicted by the different models. The focus is on particle dispersion and mean particle motion in the direction normal to the walls of the channel. For a low Reynolds number, it is shown that the turbophoresis and particle velocity fluctuations are reduced compared to DNS, if the filtered fluid velocity calculated in the LES is used in the particle equation of motion. This is a combined effect of the disregard of the subgrid scales in the fluid velocity and the inadequacy of the subgrid model. Better agreement with DNS is obtained if an inverse filtering model, which was recently proposed, is incorporated into the particle equation. This model is shown to enhance turbophoresis and particle velocity fluctuations in actual LES. The results of the approximate deconvolution model ͑ADM͒ agree better with DNS results than results of the dynamic eddy-viscosity model. This can be explained from the better prediction of the fluid velocity statistics by ADM and the better correspondence of the subgrid models adopted in the fluid and particle equations. Although the differences between the two subgrid models become smaller, similar conclusions are obtained at a higher Reynolds number. Compared to fourth-order interpolation of the fluid velocity to the particle position, second-order interpolation approximately cancels the effect of the subgrid model in the particle equation of motion.

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Section: B Particlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subgrid modeling in particle-laden channel flow

2006

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“…Ray & Collins (2011) demonstrated that a simple rescaling of the Stokes numbers based on the timescales of the resolved velocity could not reproduce the Lagrangian statistics of the particle motions because there is nonlinear coupling between the particle velocities and positions. The approximate deconvolution method could recover the one-particle statistics and, to some extent, improve the prediction of two-particle statistics with separations approximately equal to the grid resolution (Kuerten & Vreman 2005, Kuerten 2006, Shotorban et al 2007). In particular, the dynamic approximate deconvolution method based on elliptic differential filters could better predict the statistics of the preferential concentrations (Park et al 2015).…”

Section: Fdns Filtered Direct Numerical Simulationmentioning

confidence: 99%

Space-Time Correlations and Dynamic Coupling in Turbulent Flows

Jin

Yang

2017

Annu. Rev. Fluid Mech.

138

100

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Space-time correlation is a staple method for investigating the dynamic coupling of spatial and temporal scales of motion in turbulent flows. In this article, we review the space-time correlation models in both the Eulerian and Lagrangian frames of reference, which include the random sweeping and local straining models for isotropic and homogeneous turbulence, Taylor's frozen-flow model and the elliptic approximation model for turbulent shear flows, and the linear-wave propagation model and swept-wave model for compressible turbulence. We then focus on how space-time correlations are used to develop time-accurate turbulence models for the large-eddy simulation of turbulence-generated noise and particle-laden turbulence. We briefly discuss their applications to two-point closures for Kolmogorov's universal scaling of energy spectra and to the reconstruction of space-time energy spectra from a subset of spatial and temporal signals in experimental measurements. Finally, we summarize the current understanding of space-time correlations and conclude with future issues for the field.

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“…The deconvolution approach can partially recover the unsolved velocity fields. Recently, Kuerten and Verman (2005) and Shotorban and Mashayek (2006) have developed a stochastic Lagrangian particle model to better represent Lagrangian statistics of inertial particles. Fede and Simonin (2006) found that the particle accumulation and collision rate are significantly influenced by the SGS velocity fluctuations when the particle response time is of the same order or smaller than the subgrid Lagrangian integral time scale measured along particle path.…”

Section: Point-particle Based Lesmentioning

confidence: 99%

Turbulent collision of inertial particles: Point-particle based, hybrid simulations and beyond

Wang

Rosa

Gao

et al. 2009

International Journal of Multiphase Flow

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a b s t r a c t Point-particle based direct numerical simulation (PPDNS) has been a productive research tool for studying both single-particle and particle-pair statistics of inertial particles suspended in a turbulent carrier flow. Here we focus on its use in addressing particle-pair statistics relevant to the quantification of turbulent collision rate of inertial particles. PPDNS is particularly useful as the interaction of particles with small-scale (dissipative) turbulent motion of the carrier flow is mostly relevant. Furthermore, since the particle size may be much smaller than the Kolmogorov length of the background fluid turbulence, a large number of particles are needed to accumulate meaningful pair statistics. Starting from the relative simple Lagrangian tracking of so-called ghost particles, PPDNS has significantly advanced our theoretical understanding of the kinematic formulation of the turbulent geometric collision kernel by providing essential data on dynamic collision kernel, radial relative velocity, and radial distribution function. A recent extension of PPDNS is a hybrid direct numerical simulation (HDNS) approach in which the effect of local hydrodynamic interactions of particles is considered, allowing quantitative assessment of the enhancement of collision efficiency by fluid turbulence. Limitations and open issues in PPDNS and HDNS are discussed. Finally, on-going studies of turbulent collision of inertial particles using large-eddy simulations and particle-resolved simulations are briefly discussed.

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Can turbophoresis be predicted by large-eddy simulation?

Cited by 110 publications

References 16 publications

Subgrid modeling in particle-laden channel flow

Subgrid modeling in particle-laden channel flow

Space-Time Correlations and Dynamic Coupling in Turbulent Flows

Turbulent collision of inertial particles: Point-particle based, hybrid simulations and beyond

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