A dynamic spectrally enriched subgrid-scale model for preferential concentration in particle-laden turbulence (original) (raw)
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Particle subgrid scale modelling in large-eddy simulations of particle-laden turbulence
Journal of Turbulence, 2014
This thesis is concerned with particle subgrid scale (SGS) modeling in large-eddy simulation (LES) of particle-laden turbulence. Although most particle-laden LES studies have neglected the effect of the subgrid scales 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. (2004), Shotorban and Mashayek (2006) and Berrouk et al. (2007) are analyzed. The particle SGS models are assessed by conducting both a priori and a posteriori tests of a periodic box of decaying, homogeneous and isotropic turbulence with an initial Reynolds number of Re λ = 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 energy, but show a large range of results dependent on Stokes number and filter size. The stochastic models generally perform best at small Stokes numbers. Due to the random component, the stochastic models are unable to predict preferential concentration.
International Journal of Multiphase Flow, 2009
A numerical study based on the Eulerian-Lagrangian formulation is performed for dispersed phase motion in a turbulent flow. The effect of spatial filtering, commonly employed in large-eddy simulations, and the role of the subgrid scale turbulence on the statistics of heavy particles, including preferential concentration, are studied through a priori analysis of DNS of particle-laden forced isotropic turbulence. In simulations where the subgrid scale kinetic energy attains 30-35% of the total we observe the impact of residual fluid motions on particles of a smaller inertia. It is shown that neglecting the influence of subgrid scale fluctuations has a significant effect on the preferential concentration of those particles. A stochastic Langevin model is proposed to reconstruct the residual (or subgrid scale) fluid velocity along particle trajectories. The computation results for a selection of particle inertia parameters are performed to appraise the model through comparisons of particle turbulent kinetic energy and the statistics of preferential concentrations.
Stochastic modeling of the turbulent subgrid fluid velocity along inertial particle trajectories
Proceedings of the …, 2006
Large Eddy Simulation (LES) coupled with discrete particle simulation (DPS) has emerged as a powerful tool for the numerical prediction of particle dynamics in turbulent flows. To further advance the technique, several issues require investigation. These include, for the fluid phase, the effect of the particles on subgrid-scale fluid turbulence, and for the particulate phase, the effect of the subgrid fluid turbulence on particle dispersion and inter-particle collision rates. The present study focuses on the modeling of the subgrid fluid velocity fluctuations along inertial particle trajectories. The focus of this work is particles with relaxation times close to the subgrid turbulent time scale of the fluid. A Langevin model has been derived that ensures that the resulting equation for the variance of the subgrid velocity along particle paths is consistent with the mean subgrid kinetic energy equation derived from the filtered Navier-Stokes system. To assess the model, one-and two-point statistics measured from discrete particle simulations using fluid velocity fields computed using DNS of homogeneous isotropic turbulence are compared with results obtained using filtered velocity fields (obtained from the DNS ones) and the stochastic Langevin equation for the subgrid velocity reconstruction. The results show that the stochastic subgrid model enables accurate prediction of the particle kinetic energy, with a reasonable match to the DNS database. In contrast, the PDF of the particle concentration undergoes relatively stronger modifications due to the incorporation of the model, with the simulations showing that the random contribution added by the stochastic model is over-predicted. † ONERA-CERT,
Stochastic modeling of turbulent subgrid fluid velocity along inertia particle trajectories
Large Eddy Simulation (LES) coupled with discrete particle simulation (DPS) has emerged as a powerful tool for the numerical prediction of particle dynamics in turbulent flows. To further advance the technique, several issues require investigation. These include, for the fluid phase, the effect of the particles on subgrid-scale fluid turbulence, and for the particulate phase, the effect of the subgrid fluid turbulence on particle dispersion and inter-particle collision rates. The present study focuses on the modeling of the subgrid fluid velocity fluctuations along inertial particle trajectories. The focus of this work is particles with relaxation times close to the subgrid turbulent time scale of the fluid. A Langevin model has been derived that ensures that the resulting equation for the variance of the subgrid velocity along particle paths is consistent with the mean subgrid kinetic energy equation derived from the filtered Navier-Stokes system. To assess the model, one-and two-point statistics measured from discrete particle simulations using fluid velocity fields computed using DNS of homogeneous isotropic turbulence are compared with results obtained using filtered velocity fields (obtained from the DNS ones) and the stochastic Langevin equation for the subgrid velocity reconstruction. The results show that the stochastic subgrid model enables accurate prediction of the particle kinetic energy, with a reasonable match to the DNS database. In contrast, the PDF of the particle concentration undergoes relatively stronger modifications due to the incorporation of the model, with the simulations showing that the random contribution added by the stochastic model is over-predicted. † ONERA-CERT,
A stochastic subgrid model with application to turbulent flow and scalar mixing
Physics of Fluids, 2007
A new computationally cheap stochastic Smagorinsky model which allows for backscatter of subgrid scale energy is proposed. The new model is applied in the large eddy simulation of decaying isotropic turbulence, rotating homogeneous shear flow and turbulent channel flow at Re = 360. The results of the simulations are compared to direct numerical simulation data. The inclusion of stochastic backscatter has no significant influence on the development of the kinetic energy in homogeneous flows, but it improves the prediction of the fluctuation magnitudes as well as the anisotropy of the fluctuations in turbulent channel flow compared to the standard Smagorinsky model with wall damping of C S. Moreover, the stochastic model improves the description of the energy transfer by reducing its length scale and increasing its variance. Some improvements were also found in isotropic turbulence where the stochastic contribution improved the shape of the enstrophy spectrum at the smallest resolved scales and reduced the time scale of the smallest resolved scales in better agreement with earlier observations.
A subgrid Lagrangian stochastic model for turbulent passive and reactive scalar dispersion
International Journal of Heat and Fluid Flow, 2006
A large-eddy simulation (LES) with the dynamic Smagorinsky-Germano subgrid-scale (SGS) model is used to study passive and reactive scalar dispersion in a turbulent boundary layer. Instead of resolving the scalar transport equation, fluid particles containing scalar are tracked in a Lagrangian way. The Lagrangian velocity of each fluid particle is considered to have a large-scale part (directly computed by the LES) and a small-scale part. The movement of fluid elements containing scalar at a subgrid level is given by a three-dimensional Langevin model. The stochastic model is written in terms of SGS statistics at a mesh level. Diffusion is taken into account by a particle pairing exchange model. A second order, irreversible chemical reaction is considered to take place within each fluid particle. The mixing fraction, that behaves as a passive scalar is compared to the experimental results of [Fackrell, J.E., Robins, A.G., 1982. Concentration fluctuations and fluxes in plumes from point sources in a turbulent boundary layer. J. Fluid Mech. 117, 1-26] and to the LES of [Sykes, R.I., Henn, D.S., 1992. Large-eddy simulation of the concentration fluctuations in a dispersing plume. Atmos. Environ. 26A, 3127-3144]. A model for the intensity of segregation is presented and the results of the computations are in good agreement with the model. Finally, the spatial evolution of the intensity of segregation is compared to the dynamic and reactive scalar LES of [Meeder, J.P., Nieuwstadt, F.T.M., 2000. Large-eddy simulation of the turbulent dispersion of a reactive plume from a point source into a neutral atmospheric boundary layer. Atmos. Environ. 34, 3563-3573].
Physica Scripta, 2010
Particle-laden turbulent flow is a typical non-equilibrium process characterized by particle relaxation time τ p and the characteristic timescale of the flows τ f , in which the turbulent mixing of heavy particles is related to different scales of fluid motions. The preferential concentration (PC) of heavy particles could be strongly affected by fluid motion at dissipation-range scales, which presents a major challenge to the large-eddy simulation (LES) approach. The errors in simulated PC by LES are due to both filtering and the subgrid scale (SGS) eddy viscosity model. The former leads to the removal of the SGS motion and the latter usually results in a more spatiotemporally correlated vorticity field. The dependence of these two factors on the flow Reynolds number is assessed using a priori and a posteriori tests, respectively. The results suggest that filtering is the dominant factor for the under-prediction of the PC for Stokes numbers less than 1, while the SGS eddy viscosity model is the dominant factor for the over-prediction of the PC for Stokes numbers between 1 and 10. The effects of the SGS eddy viscosity model on the PC decrease as the Reynolds number and Stokes number increase. LES can well predict the PC for particle Stokes numbers larger than 10. An SGS model for particles with small and intermediate Stokes numbers is needed to account for the effects of the removed SGS turbulent motion on the PC.
Stochastic modeling of the subgrid fluid velocity fluctuations along inertial particle trajectories
2006
Large Eddy Simulation (LES) coupled with discrete particle simulation (DPS) has emerged as a powerful tool for the numerical prediction of particle dynamics in turbulent flows. To further advance the technique, several issues require investigation. These include, for the fluid phase, the effect of the particles on subgrid-scale fluid turbulence, and for the particulate phase, the effect of the subgrid fluid turbulence on particle dispersion and inter-particle collision rates. The present study focuses on the modeling of the subgrid fluid velocity fluctuations along inertial particle trajectories. The focus of this work is particles with relaxation times close to the subgrid turbulent time scale of the fluid. A Langevin model has been derived that ensures that the resulting equation for the variance of the subgrid velocity along particle paths is consistent with the mean subgrid kinetic energy equation derived from the filtered Navier-Stokes system. To assess the model, one-and two-point statistics measured from discrete particle simulations using fluid velocity fields computed using DNS of homogeneous isotropic turbulence are compared with results obtained using filtered velocity fields (obtained from the DNS ones) and the stochastic Langevin equation for the subgrid velocity reconstruction. The results show that the stochastic subgrid model enables accurate prediction of the particle kinetic energy, with a reasonable match to the DNS database. In contrast, the PDF of the particle concentration undergoes relatively stronger modifications due to the incorporation of the model, with the simulations showing that the random contribution added by the stochastic model is over-predicted. † ONERA-CERT,
Journal of Fluid Mechanics, 2000
The dynamics of subgrid-scale energy transfer in turbulence is investigated in a database of a planar turbulent jet at Reλ ≈ 110, obtained by direct numerical simulation. In agreement with analytical predictions (Kraichnan 1976), subgrid-scale energy transfer is found to arise from two effects: one involving non-local interactions between the resolved scales and disparate subgrid scales, the other involving local interactions between the resolved and subgrid scales near the cutoff. The former gives rise to a positive, wavenumber-independent eddy-viscosity distribution in the spectral space, and is manifested as low-intensity, forward transfers of energy in the physical space. The latter gives rise to positive and negative cusps in the spectral eddy-viscosity distribution near the cutoff, and appears as intense and coherent regions of forward and reverse transfer of energy in the physical space. Only a narrow band of subgrid wavenumbers, on the order of a fraction of an octave, make ...