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Direct particle–fluid simulation of Kolmogorov-length-scale size particles in decaying isotropic turbulence

Published online by Cambridge University Press:  18 April 2017

Lennart Schneiders*
Affiliation:
Institute of Aerodynamics, RWTH Aachen University, Wüllnerstrasse 5a, 52062 Aachen, Germany
Matthias Meinke
Affiliation:
Institute of Aerodynamics, RWTH Aachen University, Wüllnerstrasse 5a, 52062 Aachen, Germany JARA–HPC, Forschungszentrum Jülich, Jülich 52425, Germany
Wolfgang Schröder
Affiliation:
Institute of Aerodynamics, RWTH Aachen University, Wüllnerstrasse 5a, 52062 Aachen, Germany JARA–HPC, Forschungszentrum Jülich, Jülich 52425, Germany
*
Email address for correspondence: [email protected]

Abstract

The modulation of decaying isotropic turbulence by 45 000 spherical particles of Kolmogorov-length-scale size is studied using direct particle–fluid simulations, i.e. the flow field over each particle is fully resolved by direct numerical simulations of the conservation equations. A Cartesian cut-cell method is used by which the exchange of momentum and energy at the fluid–particle interfaces is strictly conserved. It is shown that the particles absorb energy from the large scales of the carrier flow while the small-scale turbulent motion is determined by the inertial particle dynamics. Whereas the viscous dissipation rate of the bulk flow is attenuated, the particles locally increase the level of dissipation due to the intense strain rate generated near the particle surfaces due to the crossing-trajectory effect. Analogously, the rotational motion of the particles decouples from the local fluid vorticity and strain-rate field at increasing particle inertia. The high level of dissipation is partially compensated by the transfer of momentum to the fluid via forces acting at the particle surfaces. The spectral analysis of the kinetic energy budget is supported by the average flow pattern about the particles showing a nearly universal strain-rate distribution. An analytical expression for the instantaneous rate of viscous dissipation induced by each particle is derived and subsequently verified numerically. Using this equation, the local balance of fluid kinetic energy around a particle of arbitrary shape can be precisely determined. It follows that two-way coupled point-particle models implicitly account for the particle-induced dissipation rate via the momentum-coupling terms; however, they disregard the actual length scales of the interaction. Finally, an analysis of the small-scale flow topology shows that the strength of vortex stretching in the bulk flow is mitigated due to the presence of the particles. This effect is associated with the energy conversion at small wavenumbers and the reduced level of dissipation at intermediate wavenumbers. Consequently, it damps the spectral flux of energy to the small scales.

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Papers
Copyright
© 2017 Cambridge University Press 

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References

Ahmed, A. M. & Elghobashi, S. 2000 On the mechanisms of modifying the structure of turbulent homogeneous shear flows by dispersed particles. Phys. Fluids 12, 2906.Google Scholar
Andersson, H. I., Zhao, L. & Barri, M. 2012 Torque-coupling and particle–turbulence interactions. J. Fluid Mech. 696, 319329.CrossRefGoogle Scholar
Balachandar, S. & Eaton, J. K. 2010 Turbulent dispersed multiphase flow. Annu. Rev. Fluid Mech. 42, 111133.Google Scholar
Batchelor, G. K. & Green, J. T. 1972 The determination of the bulk stress in a suspension of spherical particles to order c 2 . J. Fluid Mech. 56 (3), 401427.CrossRefGoogle Scholar
Bessel, F. W. 1828 Untersuchungen über die Länge des einfachen Secundenpendels. K. Akad. Wiss.Google Scholar
Boivin, M., Simonin, O. & Squires, K. D. 1998 Direct numerical simulation of turbulence modulation by particles in isotropic turbulence. J. Fluid Mech. 375, 235263.Google Scholar
Brito Gadeschi, G., Schneiders, L., Meinke, M. & Schröder, W. 2015 A numerical method for multiphysics simulations based on hierarchical Cartesian grids. J. Fluid Sci. Technol. 10 (1), JFST0002.Google Scholar
Burton, T. M. & Eaton, J. K. 2005 Fully resolved simulations of particle–turbulence interaction. J. Fluid Mech. 545, 67111.CrossRefGoogle Scholar
Buxton, O. R. H. & Ganapathisubramani, B. 2010 Amplification of enstrophy in the far field of an axisymmetric turbulent jet field. J. Fluid Mech. 651, 483502.Google Scholar
Buxton, O. R. H., Laizet, S. & Ganapathisubramani, B. 2011 The interaction between strain-rate and rotation in shear flow turbulence from inertial range to dissipative length scales. Phys. Fluids 23, 061704.CrossRefGoogle Scholar
Calabrese, R. V. & Middleman, S. 1979 The dispersion of discrete particles in a turbulent fluid field. AIChE J. 25 (6), 1025.CrossRefGoogle Scholar
ten Cate, A., Derksen, J. J., Portela, L. M. & van den Akker, H. E. A. 2004 Fully resolved simulations of colliding monodisperse spheres in forced isotropic turbulence. J. Fluid Mech. 519, 233271.Google Scholar
Chong, M. S., Perry, A. E. & Cantwell, B. J. 1990 A general classification of three-dimensional flow fields. Phys. Fluids A 2, 765777.CrossRefGoogle Scholar
Chong, M. S., Soria, J., Perry, A. E., Chacin, J., Cantwell, B. J. & Na, Y. 1998 Turbulence structures of wall-bounded shear flows found using DNS data. J. Fluid Mech. 357, 225247.Google Scholar
Csanady, G. T. 1963 Turbulent diffusion of heavy particles in the atmosphere. J. Atmos. Sci. 20, 201208.Google Scholar
Diamessis, P. J. & Nomura, K. K. 2000 Interaction of vorticity, rate-of-strain, and scalar gradient in stratified homogeneous sheared turbulence. Phys. Fluids 12 (5), 1166.CrossRefGoogle Scholar
Domaradzki, J. A. & Rogallo, R. S. 1990 Local energy transfer and nonlocal interactions in homogeneous, isotropic turbulence. Phys. Fluids A 2, 413.CrossRefGoogle Scholar
Druzhinin, O. A. 2001 The influence of particle inertia on the two-way coupling and modification of isotropic turbulence by microparticles. Phys. Fluids 13, 3738.CrossRefGoogle Scholar
Einstein, A. 1906 Eine neue Bestimmung der Moleküldimensionen. Ann. Phys. 324 (2), 289306.Google Scholar
Elghobashi, S. & Truesdell, G. C. 1992 Direct simulation of particle dispersion in a decaying isotropic turbulence. J. Fluid Mech. 242, 655700.CrossRefGoogle Scholar
Elghobashi, S. & Truesdell, G. C. 1993 On the two-way interaction between homogeneous turbulence and dispersed solid particles. I: turbulence modification. Phys. Fluids A 7, 1790.CrossRefGoogle Scholar
Elsinga, G. E. & Marusic, I. 2010 Universal aspects of small-scale motions in turbulence. J. Fluid Mech. 662, 514539.CrossRefGoogle Scholar
Eswaran, V. & Pope, S. B. 1988 An examination of forcing in direct numerical simulations of turbulence. Comput. Fluids 16, 257278.Google Scholar
Ferrante, A. & Elghobashi, S. 2003 On the physical mechanisms of two-way coupling in particle-laden isotropic turbulence. Phys. Fluids 15, 315.CrossRefGoogle Scholar
Gao, H., Li, H. & Wang, L.-P. 2013 Lattice Boltzmann simulation of turbulent flow laden with finite-size particles. Comput. Meth. Appl. Engng 65 (2), 194210.Google Scholar
Glowinski, R., Pan, T. W., Hesla, T. I., Joseph, D. D. & Périaux, J. 2001 A fictitious domain approach to the direct numerical simulation of incompressible viscous flow past moving rigid bodies: application to particulate flow. J. Comput. Phys. 169, 363426.CrossRefGoogle Scholar
Gore, R. A. & Crowe, C. T. 1989 Effect of particle size on modulating turbulence intensity. Intl J. Multiphase Flow 15 (2), 279285.CrossRefGoogle Scholar
Günther, C., Meinke, M. & Schröder, W. 2014 A flexible level-set approach for tracking multiple interacting interfaces in embedded boundary methods. Comput. Fluids 102, 182202.CrossRefGoogle Scholar
Hamlington, P. E., Schumacher, J. & Dahm, W. J. A. 2008 Direct assessment of vorticity alignment with local and nonlocal strain rates in turbulent flows. Phys. Fluids 20, 1117032.Google Scholar
Hartmann, D., Meinke, M. & Schröder, W. 2008 An adaptive multilevel multigrid formulation for Cartesian hierarchical grid methods. Comput. Fluids 37 (9), 11031125.CrossRefGoogle Scholar
Hartmann, D., Meinke, M. & Schröder, W. 2011 A strictly conservative Cartesian cut-cell method for compressible viscous flows on adaptive grids. Comput. Meth. Appl. Mech. Engng 200, 10381052.CrossRefGoogle Scholar
Hetsroni, G. & Sokolov, M. 1971 Distribution of mass, velocity, and intensity of turbulence in a two-phase turbulent jet. Trans. ASME J. Appl. Mech. 38 (2), 315327.CrossRefGoogle Scholar
Jeong, J. & Hussain, F. 1995 On the identification of a vortex. J. Fluid Mech. 285, 6994.Google Scholar
Jiménez, J., Wray, A. A., Saffman, P. G. & Rogallo, R. S. 1993 The structure of intense vorticity in isotropic turbulence. J. Fluid Mech. 255, 6590.Google Scholar
Kempe, T. & Fröhlich, J. 2012 Collision modelling for the interface-resolved simulation of spherical particles in viscous fluids. J. Fluid Mech. 709, 445489.CrossRefGoogle Scholar
Kholmyansky, M., Tsinober, A. & Yorish, S. 2001 Velocity derivatives in the atmospheric surface layer at Re 𝜆 = 104 . Phys. Fluids 13, 311.CrossRefGoogle Scholar
Lucci, F., Ferrante, A. & Elghobashi, S. 2010 Modulation of isotropic turbulence by particles of Taylor length-scale size. J. Fluid Mech. 650, 555.CrossRefGoogle Scholar
Lucci, F., Ferrante, A. & Elghobashi, S. 2011 Is Stokes number an appropriate indicator for turbulence modulation by particles of Taylor-length-scale size? Phys. Fluids 23, 025101.CrossRefGoogle Scholar
Nomura, K. K. & Post, G. K. 1998 The structure and dynamics of vorticity and rate of strain in incompressible homogeneous turbulence. J. Fluid Mech. 377, 6597.CrossRefGoogle Scholar
Orszag, S. A. 1969 Numerical methods for the simulation of turbulence. Phys. Fluids 12, II–250II–257.Google Scholar
Richter, D. H. 2015 Turbulence modification by inertial particles and its influence on the spectral energy budget in planar Couette flow. Phys. Fluids 27, 063304.CrossRefGoogle Scholar
Schneiders, L., Grimmen, J. H., Meinke, M. & Schröder, W. 2015a An efficient numerical method for fully-resolved particle simulations on high-performance computers. Proc. Appl. Maths Mech. 15 (1), 495496.Google Scholar
Schneiders, L., Günther, C., Grimmen, J., Meinke, M. & Schröder, W.2015b Sharp resolution of complex moving geometries using a multi-cut-cell viscous flow solver. AIAA Paper 2015-3427.Google Scholar
Schneiders, L., Günther, C., Meinke, M. & Schröder, W. 2016a An efficient conservative cut-cell method for rigid bodies interacting with viscous compressible flows. J. Comput. Phys. 311, 6286.Google Scholar
Schneiders, L., Hartmann, D., Meinke, M. & Schröder, W. 2013 An accurate moving boundary formulation in cut-cell methods. J. Comput. Phys. 235, 786809.Google Scholar
Schneiders, L., Meinke, M. & Schröder, W.2016b On the accuracy of Lagrangian point-mass models for heavy non-spherical particles in isotropic turbulence. Fuel (in press).CrossRefGoogle Scholar
Schumann, U. & Patterson, G. S. 1978 Numerical study of the return of axisymmetric turbulence to isotropy. J. Fluid Mech. 88, 711735.Google Scholar
Siewert, C., Kunnen, R. P. J., Meinke, M. & Schröder, W. 2014a Orientation statistics and settling velocity of ellipsoids in decaying turbulence. Atmos. Res. 142, 4556.Google Scholar
Siewert, C., Kunnen, R. P. J. & Schröder, W. 2014b Collision rates of small ellipsoids settling in turbulence. J. Fluid Mech. 685, 686701.CrossRefGoogle Scholar
da Silva, C. B. & Pereira, J. C. F. 2008 Invariants of the velocity-gradient, rate-of-strain, and rate-of-rotation tensors across the turbulent/nonturbulent interface in jets. Phys. Fluids 20, 0551012.Google Scholar
Snyder, W. H. & Lumley, J. L. 1971 Some measurements of particle velocity autocorrelation functions in a turbulent flow. J. Fluid Mech. 48, 4171.Google Scholar
Squires, K. D. & Eaton, J. K. 1990 Particle response and turbulence modification in isotropic turbulence. Phys. Fluids A 2, 1191.CrossRefGoogle Scholar
Strutt, H. C., Tullis, S. W. & Lightstone, M. F. 2011 Numerical methods for particle-laden DNS. Comput. Fluids 40, 210220.Google Scholar
Sundaram, S. & Collins, L. R. 1996 Numerical considerations in simulating a turbulent suspension of finite-volume particles. J. Comput. Phys. 124, 337350.CrossRefGoogle Scholar
Tanaka, T. & Eaton, J. K. 2008 Classification of turbulence modification by dispersed spheres using a novel dimensionless number. Phys. Rev. Lett. 101, 114502.Google Scholar
Tanaka, T. & Eaton, J. K. 2010 Sub-Kolmogorov resolution particle image velocimetry measurements of particle-laden forced turbulence. J. Fluid Mech. 643, 177206.CrossRefGoogle Scholar
Taylor, G. I. 1938 Production and dissipation of vorticity in a turbulent fluid. Proc. R. Soc. Lond. A 164, 1523.Google Scholar
Thornber, B., Mosedale, A., Drikakis, D., Youngs, D. & Williams, R. 2008 An improved reconstruction method for compressible flows with low Mach number features. J. Comput. Phys. 227 (10), 48734894.Google Scholar
Tsinober, A. 2000 Vortex stretching versus production of strain/dissipation. In Turbulence Structure and Vortex Dynamics (ed. Hunt, J. C. R. & Vassilicos, J. C.), pp. 164191. Cambridge University Press.Google Scholar
Uhlmann, M. 2008 Interface-resolved direct numerical simulation of vertical particulate channel flow in the turbulent regime. Phys. Fluids 20, 053305.Google Scholar
Vaghefi, N. S. & Madnia, C. K. 2015 Local flow topology and velocity gradient invariants in compressible turbulent mixing layer. J. Fluid Mech. 774, 6794.Google Scholar
Wang, L.-P., Ayala, O., Gao, H., Andersen, C. & Mathews, K. L. 2014 Study of forced turbulence and its modulation by finite-size solid particles using the lattice Boltzmann approach. Comput. Maths Applics. 67, 363380.Google Scholar
White, F. M. 1991 Viscous Fluid Flow. McGraw-Hill.Google Scholar
Xu, Y. & Subramaniam, S. 2007 Consistent modeling of interphase turbulent kinetic energy transfer. Phys. Fluids 19, 085101.Google Scholar
Yeo, K., Dong, S., Climent, E. & Maxey, M. R. 2010 Modulation of homogeneous turbulence seeded with finite size bubbles or particles. Intl J. Multiphase Flow 36, 221233.Google Scholar
Zisselmar, R. & Molerus, O. 1979 Investigation of solid–liquid pipe flow with regard to turbulence modification. Chem. Engng J. 18 (3), 233239.Google Scholar