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Numerical study of collisional particle dynamics in cluster-induced turbulence

Published online by Cambridge University Press:  23 April 2014

Jesse Capecelatro ,
Olivier Desjardins  and
Rodney O. Fox
Jesse Capecelatro*
Affiliation:
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853-7501, USA
Olivier Desjardins
Affiliation:
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853-7501, USA
Rodney O. Fox
Affiliation:
Department of Chemical and Biological Engineering, 2114 Sweeney Hall, Iowa State University, Ames, IA 50011-2230, USA EM2C-UPR CNRS 288, Ecole Centrale Paris, Grande vois des Vignes, 92295 Chatenay Malabry, France
*
†Email address for correspondence: [email protected]
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Abstract

We present a computational study of cluster-induced turbulence (CIT), where the production of fluid-phase kinetic energy results entirely from momentum coupling with finite-size inertial particles. A separation of length scales must be established when evaluating the particle dynamics in order to distinguish between the continuous mesoscopic velocity field and the uncorrelated particle motion. To accomplish this, an adaptive spatial filter is employed on the Lagrangian data with an averaging volume that varies with the local particle-phase volume fraction. This filtering approach ensures sufficient particle sample sizes in order to obtain meaningful statistics while remaining small enough to avoid capturing variations in the mesoscopic particle field. Two-point spatial correlations are computed to assess the validity of the filter in extracting meaningful statistics. The method is used to investigate, for the first time, the properties of a statistically stationary gravity-driven particle-laden flow, where particle–particle and fluid–particle interactions control the multiphase dynamics. Results from fully developed CIT show a strong correlation between the local volume fraction and the granular temperature, with maximum values located at the upstream boundary of clusters (i.e. where maximum compressibility of the particle velocity field exists), while negligible particle agitation is observed within clusters.

JFM classification

Turbulent Flows: Homogeneous turbulence Multiphase and Particle-laden Flows: Multiphase and Particle-laden Flows Multiphase and Particle-laden Flows: Particle/fluid flow
Type
Rapids
Information
Journal of Fluid Mechanics , Volume 747 , 25 May 2014 , R2
Copyright
© 2014 Cambridge University Press 

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References

Agrawal, K., Loezos, P. N., Syamlal, M. & Sundaresan, S. 2001 The role of meso-scale structures in rapid gas–solid flows. J. Fluid Mech. 445 (1), 151–185.CrossRefGoogle Scholar
Anderson, T. B. & Jackson, R. 1967 Fluid mechanical description of fluidized beds. Equations of motion. Ind. Engng Chem. Fundam. 6 (4), 527–539.Google Scholar
Balachandar, S. & Eaton, J. K. 2010 Turbulent dispersed multiphase flow. Annu. Rev. Fluid Mech. 42, 111–133.CrossRefGoogle Scholar
Briley, W. R. & McDonald, H. 1977 Solution of the multidimensional compressible Navier–Stokes equations by a generalized implicit method. J. Comput. Phys. 24 (4), 372–397.Google Scholar
Capecelatro, J. & Desjardins, O. 2013 An Euler–Lagrange strategy for simulating particle-laden flows. J. Comput. Phys. 238, 1–31.Google Scholar
Capecelatro, J., Pepiot, P. & Desjardins, O. 2014 Numerical characterization and modeling of particle clustering in wall-bounded vertical risers. Chem. Engng J. 245, 295–310.Google Scholar
Cundall, P. A. & Strack, O. D. L. 1979 A discrete numerical model for granular assemblies. Gèotechnique 29 (1), 47–65.Google Scholar
Dasgupta, S., Jackson, R. & Sundaresan, S. 1994 Turbulent gas–particle flow in vertical risers. AIChE J. 40 (2), 215–228.Google Scholar
Eaton, J. K. & Fessler, J. R. 1994 Preferential concentration of particles by turbulence. Intl J. Multiphase Flow 20, 169–209.Google Scholar
Février, P., Simonin, O. & Squires, K. D. 2005 Partitioning of particle velocities in gas–solid turbulent flows into a continuous field and a spatially uncorrelated random distribution: theoretical formalism and numerical study. J. Fluid Mech. 533, 1–46.Google Scholar
Fox, R. O. 2012 Large-eddy-simulation tools for multiphase flows. Annu. Rev. Fluid Mech. 44, 47–76.Google Scholar
Fox, R. O. 2014 On multiphase turbulence models for collisional fluid–particle flows. J. Fluid Mech. 742, 368–424.Google Scholar
Glasser, B. J., Sundaresan, S. & Kevrekidis, I. G. 1998 From bubbles to clusters in fluidized beds. Phys. Rev. Lett. 81, 1849.Google Scholar
Goldhirsch, I. & Zanetti, G. 1993 Clustering instability in dissipative gases. Phys. Rev. Lett. 70 (11), 1619.Google Scholar
Hopkins, M. A. & Louge, M. Y. 1991 Inelastic microstructure in rapid granular flows of smooth disks. Phys. Fluids 3 (1), 47–57.Google Scholar
Hrenya, C. M. & Sinclair, J. L. 1997 Effects of particle-phase turbulence in gas–solid flows. AIChE J. 43 (4), 853–869.Google Scholar
Igci, Y., Andrews, A. T., Sundaresan, S., Pannala, S. & O’Brien, T. 2008 Filtered two-fluid models for fluidized gas–particle suspensions. AIChE J. 54 (6), 1431–1448.Google Scholar
Maxey, M. R. 1987 The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields. J. Fluid Mech. 174, 441–465.Google Scholar
McQuarrie, D. A. 1976 Statistical Mechanics. Harper and Row.Google Scholar
Mitrano, P. P., Dahl, S. R., Hilger, A. M., Ewasko, C. J. & Hrenya, C. M. 2013 Dual role of friction in granular flows: attenuation versus enhancement of instabilities. J. Fluid Mech. 729, 484–495.Google Scholar
Ozel, A., Fede, P. & Simonin, O. 2013 Development of filtered Euler–Euler two-phase model for circulating fluidised bed: high resolution simulation, formulation and a priori analyses. Intl J. Multiphase Flow 55, 43–63.Google Scholar
Pozorski, J. & Apte, S. V. 2009 Filtered particle tracking in isotropic turbulence and stochastic modeling of subgrid-scale dispersion. Intl J. Multiphase Flow 35 (2), 118–128.Google Scholar
Royer, J. R., Evans, D. J., Oyarte, L., Guo, Q., Kapit, E., Möbius, M. E., Waitukaitis, S. R. & Jaeger, H. M. 2009 High-speed tracking of rupture and clustering in freely falling granular streams. Nature 459 (7250), 1110–1113.Google Scholar
Rumsey, C. L.2009 Compressibility considerations for $\kappa - \omega $ turbulence models in hypersonic boundary layer applications. Tech Rep. NASA/TM-2009-215705. NASA Center for AeroSpace Information.Google Scholar
Shaffer, F., Gopalan, B., Breault, R. W., Cocco, R., Karri, S. B., Hays, R. & Knowlton, T. 2013 High speed imaging of particle flow fields in CFB risers. Powder Technol. 242, 86–99.Google Scholar
Squires, K. D. & Eaton, J. K. 1991 Preferential concentration of particles by turbulence. Phys. Fluids A 3, 1169.CrossRefGoogle Scholar
Tenneti, S., Garg, R. & Subramaniam, S. 2011 Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres. Intl J. Multiphase Flow 37 (9), 1072–1092.CrossRefGoogle Scholar
Tenneti, S. & Subramaniam, S. 2014 Particle-resolved direct numerical simulation for gas–solid flow model development. Annu. Rev. Fluid Mech. 46, 199–230.Google Scholar
Tsuji, Y., Tanaka, T. & Yonemura, S. 1994 Particle induced turbulence. Appl. Mech. Rev. 47 (6), S75–S79.Google Scholar
Wilcox, D. C. 2006 Turbulence Modeling for CFD. 3rd edn. DCW Industries.Google Scholar
Wylie, J. J. & Koch, D. L. 2000 Particle clustering due to hydrodynamic interactions. Phys. Fluids 12 (5), 964–970.Google Scholar
Yin, X., Zenk, J. R., Mitrano, P. P. & Hrenya, C. M. 2013 Impact of collisional versus viscous dissipation on flow instabilities in gas–solid systems. J. Fluid Mech. 727, R2.Google Scholar

Capecelatro et al. supplementary movie

Simulation of Re=1 cluster-induced turbulence. The particles are initially randomly distributed suspended in a gas phase at rest. The mesh size is 1024 x 256 x 256, corresponding to 7 million particles. Left: particle-phase volume fraction, middle: vertical fluid-phase velocity, right: granular temperature.

Download Capecelatro et al. supplementary movie(Video)
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