On the Error Estimate in Sub-Grid Models for Particles in Turbulent Flows

Calzavarini, Enrico; Donini, A Andrea; Lavezzo, Valentina; Marchioli, Cristian; Pitton, Enrico; Soldati, Alfredo; Toschi, Federico

doi:10.1007/978-94-007-2482-2_27

Cited by 5 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same assumption has been used in other studies [12][13][14][15] in which the filtering due to particle inertia and the moderate Reynolds number of the flow had a relatively weak effect on the (one-particle, two-particles) dispersion statistics examined. However, several studies [16][17][18] have demonstrated that neglecting the effect of SGS velocity fluctuations on particle motion leads to significant errors in the quantification of large-scale clustering and preferential concentration, two macroscopic phenomena that result from particle preferential distribution at the periphery of strong vortical regions into low-strain regions [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Lagrangian filtered density function for LES-based stochastic modelling of turbulent particle-laden flows

2016

View full text Add to dashboard Cite

The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one of the most promising and viable numerical tools to study turbulent dispersed flows when the computational cost of Direct Numerical Simulation (DNS) becomes too expensive. The applicability of this approach is however limited if the effects of the Sub-Grid Scales (SGS) of the flow on particle dynamics are neglected. In this paper, we propose to take these effects into account by means of a Lagrangian stochastic SGS model for the equations of particle motion. The model extends to particle-laden flows the velocity-filtered density function method originally developed for reactive flows. The underlying filtered density function is simulated through a Lagrangian Monte Carlo procedure that solves for a set of Stochastic Differential Equations (SDEs) along individual particle trajectories. The resulting model is tested for the reference case of turbulent channel flow, using a hybrid algorithm in which the fluid velocity field is provided by LES and then used to advance the SDEs in time. The model consistency is assessed in the limit of particles with zero inertia, when "duplicate fields" are available from both the Eulerian LES and the Lagrangian tracking. Tests with inertial particles were performed to examine the capability of the model to capture particle preferential concentration and near-wall segregation. Upon comparison with DNS-based statistics, our results show improved accuracy and considerably reduced errors with respect to the case in which no SGS model is used in the equations of particle motion.2

show abstract

Section: Introductionmentioning

confidence: 99%

Lagrangian filtered density function for LES-based stochastic modelling of turbulent particle-laden flows

2016

View full text Add to dashboard Cite

show abstract

“…Reprinted from [18] removed from the fluid velocity field is small (namely, the LES is well-resolved [8]). In fact, several studies have shown that subgrid fluid velocities must be taken into account to provide reliable calculations of particle kinetic properties and/or preferential concentration phenomena [61,83].…”

Section: Governing Equations and Relevant Modelling Parametersmentioning

confidence: 99%

“…A visualization of LES spatial filtering effects is provided in Fig. 1, which shows the impact on the flow structures in homogeneous isotropic turbulence (referred to as HIT hereinafter) for different filter widths [18].…”

Section: Introductionmentioning

confidence: 99%

“…The progressive loss of small-scale details of the flow, which are not felt by the Lagrangian particles as they evolve within the fluid domain, is apparent. Reprinted from [18] to describe the physics of the dispersed phase in terms of kinetic equations. The macroscopic model is then obtained from the mesoscopic model using an averaging procedure to obtain the moments of the density function appearing in the kinetic equations (for instance, the zero-order moment of the density function is related to concentration, the first-order moment to the mean momentum) [35].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Large-eddy simulation of turbulent dispersed flows: a review of modelling approaches

Marchioli

2017

Acta Mech

View full text Add to dashboard Cite

In large-eddy simulation (LES) of turbulent dispersed flows, modelling and numerical inaccuracies are incurred because LES provides only an approximation of the filtered velocity. Interpolation errors can also occur (on coarse-grained domains, for instance). These inaccuracies affect the estimation of the forces acting on particles, obtained when the filtered fluid velocity is supplied to the Lagrangian equation of particle motion, and accumulate in time. As a result, particle trajectories in LES fields progressively diverge from particle trajectories in DNS fields, which can be considered as the exact numerical reference: the flow fields seen by the particles become less and less correlated, and the forces acting on particles are evaluated at increasingly different locations. In this paper, we review models and strategies that have been proposed in the EulerianLagrangian framework to correct the above-mentioned sources of inaccuracy on particle dynamics and to improve the prediction of particle dispersion in turbulent dispersed flows.

show abstract

“…To remove this effect, we perform here a-priori tests in which: (i) particle trajectories are computed from the DNS fields, (ii) the DNS fields are coarse-grained through filtering and, (iii) particles are forced to evolve in the filtered DNS fields along the DNS trajectories. A similar a-priori analysis has been performed recently to evaluate pure filtering error and time accumulation error for the case of tracer particle dispersion in homogeneous isotropic turbulence 20 . In this paper, we focus on inertial particles in turbulent channel flow.…”

Section: Reynolds Numbers Demands Alternative Approaches Among Whichmentioning

confidence: 99%

Intrinsic filtering errors of Lagrangian particle tracking in LES flow fields

et al. 2012

Self Cite

View full text Add to dashboard Cite

1 Large-Eddy Simulations (LES) of two-phase turbulent flows exhibit quantitative differences in particle statistics if compared to Direct Numerical Simulations (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 subgrid-scale velocity field on the force driving the particles, and establish the requirements which a LES model must satisfy to predict correctly the velocity and the trajectory of inertial particles.2

show abstract

On the Error Estimate in Sub-Grid Models for Particles in Turbulent Flows

Cited by 5 publications

References 2 publications

Lagrangian filtered density function for LES-based stochastic modelling of turbulent particle-laden flows

Lagrangian filtered density function for LES-based stochastic modelling of turbulent particle-laden flows

Large-eddy simulation of turbulent dispersed flows: a review of modelling approaches

Intrinsic filtering errors of Lagrangian particle tracking in LES flow fields

Contact Info

Product

Resources

About