Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T10:36:53.425Z Has data issue: false hasContentIssue false

Acceleration in turbulent channel flow: universalities in statistics, subgrid stochastic models and an application

Published online by Cambridge University Press:  21 March 2013

Rémi Zamansky
Affiliation:
Laboratoire de Mécanique des Fluides et d’Acoustique, Université de Lyon, Université Claude-Bernard Lyon 1/CNRS/Ecole Centrale de Lyon/INSA-Lyon, 69134 Ecully, France
Ivana Vinkovic
Affiliation:
Laboratoire de Mécanique des Fluides et d’Acoustique, Université de Lyon, Université Claude-Bernard Lyon 1/CNRS/Ecole Centrale de Lyon/INSA-Lyon, 69134 Ecully, France
Mikhael Gorokhovski*
Affiliation:
Laboratoire de Mécanique des Fluides et d’Acoustique, Université de Lyon, Université Claude-Bernard Lyon 1/CNRS/Ecole Centrale de Lyon/INSA-Lyon, 69134 Ecully, France
*
Email address for correspondence: [email protected]

Abstract

This paper focuses on the characterization and the stochastic modelling of the fluid acceleration in turbulent channel flow. In the first part, the acceleration is studied by direct numerical simulation (DNS) at three Reynolds numbers (${\mathit{Re}}_{\ast } = {u}_{\ast } h/ \nu = 180$, 590 and 1000). It is observed that whatever the wall distance is, the norm of acceleration is log-normally distributed and that the variance of the norm is very close to its mean value. It is also observed that from the wall to the centreline of the channel, the orientation of acceleration relaxes statistically towards isotropy. On the basis of dimensional analysis, a universal scaling law for the acceleration norm is proposed. In the second part, in the framework of the norm/orientation decomposition, a stochastic model of the acceleration is introduced. The stochastic model for the norm is based on fragmentation process which evolves across the channel with the wall distance. Simultaneously the orientation is simulated by a random walk on the surface of a unit sphere. The process is generated in such a way that the mean components of the orientation vector are equal to zero, whereas with increasing wall distance, all directions become equally probable. In the third part, the models are assessed in the framework of large-eddy simulation with stochastic subgrid acceleration model (LES–SSAM), introduced recently by Sabel’nikov, Chtab-Desportes & Gorokhovski (Euro. Phys. J. B, vol. 80, 2011, p. 177–187), and designed to account for the intermittency at subgrid scales. Computations by LES–SSAM and its assessment using DNS data show that the prediction of important statistics to characterize the flow, such as the mean velocity, the energy spectra at small scales, the viscous and turbulent stresses, the distribution of the acceleration can be considerably improved in comparison with standard LES. In the last part of this paper, the advantage of LES–SSAM in accounting for the subgrid flow structure is demonstrated in simulation of particle-laden turbulent channel flows. Compared to standard LES, it is shown that for different Stokes numbers, the particle dynamics and the turbophoresis effect can be predicted significantly better when LES–SSAM is applied.

Type
Papers
Copyright
©2013 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Present address: Center for Turbulence Research, Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.

References

Afonso, M. M. & Meneveau, C. 2010 Recent fluid deformation closure for velocity gradient tensor dynamics in turbulence: timescale effects and expansions. Physica D 239, 12411250.CrossRefGoogle Scholar
Antonia, R. A., Kim, J. & Browne, L. W. B. 1991 Some characteristics of small-scale turbulence in a turbulent duct flow. J. Fluid Mech. 233, 369388.Google Scholar
Armenio, V., Piomelli, U. & Fiorotto, V. 1999 Effect of the subgrid scales on particle motion. Phys. Fluids 11 (10), 30303042.Google Scholar
Barenblatt, G. I. 1993 Scaling laws for fully developed turbulent shear flows. Part 1. Basic hypotheses and analysis. J. Fluid Mech. 248, 513520.Google Scholar
Barenblatt, G. I. 1996 Scaling, Self-similarity, and Intermediate Asymptotics. Cambridge University Press.Google Scholar
Barenblatt, G. I. & Prostokishin, V. M. 1993 Scaling laws for fully developed turbulent shear flows. Part 2. Processing of experimental data. J. Fluid Mech. 248, 521529.Google Scholar
Brasseur, J. G. & Wei, T. 2010 Designing large-eddy simulation of the turbulent boundary layer to capture law-of-the-wall scaling. Phys. Fluids 22 (2), 021303.Google Scholar
Buffat, M., Le Penven, L. & Cadiou, A. 2011 An efficient spectral method based on an orthogonal decomposition of the velocity for transition analysis in wall bounded flow. Comput. Fluids 42, 6272.Google Scholar
Burton, G. C. & Dahm, W. J. A. 2005a Multifractal subgrid-scale modeling for large-eddy simulation. I. Model development and a priori testing. Phys. Fluids 17, 075111.CrossRefGoogle Scholar
Burton, G. C. & Dahm, W. J. A. 2005b Multifractal subgrid-scale modeling for large-eddy simulation. II. Backscatter limiting and a posteriori evaluation. Phys. Fluids 17, 075112.Google Scholar
Champagne, F. H., Harris, V. G. & Corsin, S. 1970 Experiments on nearly homogeneous turbulent shear flow. J. Fluid Mech. 41, 81139.Google Scholar
Chaouat, B. & Schiestel, R. 2009 Further insight into sub-grid scale transport for continuous hybrid non-zonal RANS/LES simulations. In Proceeding of the Sixth Internal Symposium on Turbulence and Shear Flow Phenomena (TSFP 6) (ed. N. Kasagi, J. K. Eaton, J. A. C. Humphrey, A. V. Johansson & H. J. Sung), pp. 1063–1068.Google Scholar
Chen, L., Coleman, S. W., Vassilicos, J. C. & Hu, Z. 2010 Acceleration in turbulent channel flow. J. Turbul. 11 (N41).CrossRefGoogle Scholar
Chevillard, L. & Meneveau, C. 2006 Recent fluid deformation closure for velocity gradient tensor dynamics in turbulence: timescale effects and expansions. Phys. Rev. Lett. 97, 174501.CrossRefGoogle Scholar
Choi, J.-I., Yeo, K. & Lee, C. 2004 Lagrangian statistics in turbulent channel flow. Phys. Fluids 16 (3), 779793.Google Scholar
Christensen, K. & Adrian, R. 2002 The velocity and acceleration signatures of small-scale vortices in turbulent channel flow. J. Turbul. 3, 2729.Google Scholar
Clift, R., Grace, J. & Weber, M. 1978 Bubble, Drops and Particles. Academic.Google Scholar
Crawford, A. M., Mordant, N. & Bodenschatz, E. 2005 Joint statistics of the Lagrangian acceleration and velocity in fully developed turbulence. Phys. Rev. Lett. 94 (2), 024501.Google Scholar
Frisch, U. 1995 Turbulence: The Legacy of A. N. Kolmogorov. Cambridge University Press.Google Scholar
Germano, M., Piomelli, U., Moin, P. & Cabot, W. H. 1991 A dynamic subgrid-scale eddy viscosity model. Phys. Fluids A 3 (7), 17601765.Google Scholar
Gorokhovski, M. 2003 Fragmentation under the scaling symmetry and turbulent cascade with intermittency. In Annual Research Briefs 2003, pp. 197–203. Stanford University: Center for Turbulence Research.Google Scholar
Gorokhovski, M. A. & Saveliev, V. L. 2008 Statistical universalities in fragmentation under scaling symmetry with a constant frequency of fragmentation. J. Phys. D: Appl. Phys. 41, 085405.Google Scholar
Hill, R. J. 2002 Scaling of acceleration in locally isotropic turbulence. J. Fluid Mech. 452, 361370.Google Scholar
Hoyas, S. & Jiménez, J. 2008 Reynolds number effects on the Reynolds-stress budgets in turbulent channels. Phys. Fluids 20 (10), 101511.Google Scholar
Hughes, T. J. R., Oberai, A. A. & Mazzei, L. 2001 Large eddy simulation of turbulent channel flows by the variational multiscale method. Phys. Fluids 13 (6), 17841799.Google Scholar
Jiménez, J. 2012 Cascades in wall-bounded turbulence. Annu. Rev. Fluid Mech. 44, 2745.Google Scholar
Kadanoff, L. P. 2000 Statistical Physics, Statics, Dynamics and Renormalization. World Scientific.Google Scholar
Kemenov, K. A. & Menon, S. 2006 Explicit small-scale velocity simulation for high-Re turbulent flows. J. Comput. Phys. 220, 290311.Google Scholar
Kemenov, K. A. & Menon, S. 2007 Explicit small-scale velocity simulation for high-Re turbulent flows. Part II: Non-homogeneous flows. J. Comput. Phys. 222, 673701.CrossRefGoogle Scholar
Kerstein, A. R. 1999 One-dimensional turbulence: model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows. J. Fluid Mech. 392, 277334.Google Scholar
Kerstein, A. R. 2002 One-dimensional turbulence: a new approach to high-fidelity subgrid closure of turbulent flow simulations. Comput. Phys. Commun. 148, 116.Google Scholar
Kuerten, J. G. M. 2006 Subgrid modelling in particle-laden channel flow. Phys. Fluids 18, 025108.Google Scholar
Kuerten, J. G. M. & Vreman, A. W. 2005 Can turbophoresis be predicted by large-eddy simulation? Phys. Fluids 17, 011701.Google Scholar
Lamorgese, A. G., Pope, S. B., Yeung, P. K. & Sawford, B. L. 2007 A conditionally cubic-Gaussian stochastic Lagrangian model for acceleration in isotropic turbulence. J. Fluid Mech. 582, 243448.Google Scholar
Landau, L. D. & Lifshitz, E. M. 1987 Fluid Mechanics, 2nd edn. Course of Theoretical Physics, Volume 6, Pergamon.Google Scholar
Larsson, J., Lien, F. S. & Yee, E. 2007 Feedback-controlled forcing in hybrid LES/RANS. Intl J. Comput. Fluid Dyn. 20, 687699.Google Scholar
Lee, C., Yeo, K. & Choi, J.-I. 2004 Intermittent nature of acceleration in near-wall turbulence. Phys. Rev. Lett. 92 (14), 144502.Google Scholar
Lévêque, E., Toschi, F., Shao, L. & Bertoglio, J.-P. 2007 Shear-improved Smagorinsky model for large-eddy simulation of wall-bounded turbulent flows. J. Fluid Mech. 570, 491502.Google Scholar
Lilly, D. K. 1992 A proposed modification of the Germano subgrid-scale closure method. Phys. Fluids A 4 (3), 633635.CrossRefGoogle Scholar
Marchioli, C., Salvetti, M. V. & Soldati, A. 2008 Some issues concerning large-eddy simulation of inertial particle dispersion in turbulent bounded flows. Phys. Fluids 20 (4), 040603.Google Scholar
Marchioli, C. & Soldati, A. 2002 Mechanisms for particle transfer and segregation in a turbulent boundary layer. J. Fluid Mech. 468, 283315.Google Scholar
Marusic, I., McKeon, B. J., Monkewitz, P. A., Nagib, H. M., Smits, A. J. & Sreenivasan, K. R. 2010 Wall-bounded turbulent flows at high Reynolds numbers: recent advances and key issues. Phys. Fluids 22 (6), 065103.Google Scholar
Meneveau, C. & Katz, J. 2000 Scale-invariance and turbulence models for large-eddy simulation. Annu. Rev. Fluid Mech. 32, 132.Google Scholar
Monin, A. S. & Yaglom, A. M. 1981 Statistical Fluid Mechanics: Mechanics of Turbulence, vol. 2. MIT.Google Scholar
Mordant, N., Crawford, A. M. & Bodenschatz, E. 2004a Experimental Lagrangian acceleration probability density function measurement. Physica D 193 (1–4), 245251.Google Scholar
Mordant, N., Crawford, A. M. & Bodenschatz, E. 2004b Three-dimensional structure of the Lagrangian acceleration in turbulent flows. Phys. Rev. Lett. 93 (21), 214501.Google Scholar
Mordant, N., Delour, J., Lévêque, E., Arnéodo, A. & Pinton, J.-F. 2002 Long time correlations in Lagrangian dynamics: a key to intermittency in turbulence. Phys. Rev. Lett. 89 (25), 254502.CrossRefGoogle ScholarPubMed
Moser, R. D., Kim, J. & Mansour, N. N. 1999 Direct numerical simulation of turbulent channel flow up to ${\mathit{Re}}_{\tau } = 590$ . Phys. Fluids 11 (4), 943945.Google Scholar
Moser, R. D., Moin, P. & Leonard, A. 1983 A spectral numerical method for the Navier–Stokes equations with applications to Taylor–Couette flow. J. Comput. Phys. 52, 524544.Google Scholar
Park, N. & Mahesh, K. 2008 A velocity-estimation subgrid model constrained by subgrid scale dissipation. J. Comput. Phys. 227, 41904206.Google Scholar
Pinsky, M., Khain, A. & Tsinober, A. 2000 Accelerations in isotropic and homogeneous turbulence and Taylor’s hypothesis. Phys. Fluids 12 (12), 31953204.Google Scholar
Piomelli, U. & Balaras, E. 2002 Wall-layer models for large-eddy simulations. Annu. Rev. Fluid Mech. 34, 349374.Google Scholar
Piomelli, U., Balaras, E., Pasinato, H., Squires, K. D. & Spalart, P. R. 2003 The inner-outer layer interface in large-eddy simulations with wall-layer models. Intl J. Heat Fluid Flow 24 (4), 538550.Google Scholar
Pope, S. B. 1990 Lagrangian microscales in turbulence. Phil. Trans. R. Soc. Lond. 333 (1631), 309319.Google Scholar
Pope, S. B. 1991 Application of the velocity-dissipation probability density function model to inhomogeneous turbulent flows. Phys. Fluids A 3 (8), 19471957.CrossRefGoogle Scholar
Pope, S. B. 2000 Turbulent Flows. Cambridge University Press.Google Scholar
Pope, S. B. & Chen, Y. L. 1990 The velocity-dissipation probability density function model for turbulent flows. Phys. Fluids 2 (8), 14371449.Google Scholar
Pozorski, J. & Apte, S. V. 2009 Filtered particle tracking in isotropic turbulence and stochastic modelling of subgrid-scale dispersion. Intl J. Multiphase Flow 35 (2), 118128.Google Scholar
Reynolds, A. M., Mordant, N., Crawford, A. M. & Bodenschatz, E. 2005 On the distribution of Lagrangian accelerations in turbulent flows. New J. Phys. 7 (1), 58.Google Scholar
Sabel’nikov, V., Chtab-Desportes, A. & Gorokhovski, M. 2011 New sub-grid stochastic acceleration model in LES of high-Reynolds-number flows. Eur. Phys. J. B 80 (2), 177187.Google Scholar
Sagaut, P. 2002 Large Eddy Simulation for Incompressible Flows: An Introduction, 2nd edn. Springer.Google Scholar
Sagaut, P., Montreuil, E. & Labbé, O. 1999 Assessment of some self-adaptive SGS models for wall bounded flows. Aerosp. Sci. Technol. 6, 335344.Google Scholar
Sarghini, F., Piomelli, U. & Balaras, E. 1999 Scale-similar models for large-eddy simulations. Phys. Fluids 11 (6), 15961607.Google Scholar
Saveliev, V. L. & Gorokhovski, M. A. 2005 Group-theoretical model of developed turbulence and renormalization of the Navier–Stokes equation. Phys. Rev. E 72, 016302.CrossRefGoogle ScholarPubMed
Saveliev, V. L. & Gorokhovski, M. A. 2012 Renormalization of the fragmentation equation: exact self-similar solutions and turbulent cascades. Phys. Rev. E 86, 061112.Google Scholar
Sawford, B. L., Yeung, P. K., Borgas, M. S., Vedula, P., La Porta, A., Crawford, A. M. & Bodenschatz, E. 2003 Conditional and unconditional acceleration statistics in turbulence. Phys. Fluids 15 (11), 34783489.Google Scholar
Schmidt, R. C., Kerstein, A. R. & Wunsch, S. 2003 Near-wall LES closure based on one-dimensional turbulence modelling. J. Comput. Phys. 186, 317355.Google Scholar
Shur, M. L., Spalart, P. R., Strelets, M. Kh. & Travin, A. K. 2008 A hybrid RANS-LES approach with delayed-DES and wall-modelled LES capabilities. Intl J. Heat Fluid Flow 29 (6), 16381649.Google Scholar
Smits, A, J., McKeon, B. J. & Marusic, I. 2011 High-Reynolds Number Wall Turbulence. Annu. Rev. Fluid Mech. 43, 353375.Google Scholar
Spalart, P. R., Deck, S., Shur, M. L., Squires, K. D., Strelets, M. Kh. & Travin, A. 2006 A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theor. Comput. Fluid Dyn. 20, 181195.CrossRefGoogle Scholar
Toschi, F. & Bodenschatz, E. 2009 Lagrangian properties of particles in turbulence. Annu. Rev. Fluid Mech. 41 (1), 375404.Google Scholar
Tsinober, A., Vedula, P. & Yeung, P. K. 2001 Random taylor hypothesis and the behaviour of local and convective accelerations in isotropic turbulence. Phys. Fluids 13 (7), 19741984.CrossRefGoogle Scholar
Vedula, P. & Yeung, P. K. 1999 Similarity scaling of acceleration and pressure statistics in numerical simulations of isotropic turbulence. Phys. Fluids 11 (5), 12081220.Google Scholar
Vinkovic, I., Aguirre, C., Ayrault, M. & Simoëns, S. 2006 Large-eddy simulation of the dispersion of solid particles in a turbulent boundary layer. Boundary-Layer Meteorol. 121, 283311.Google Scholar
Volker, S., Moser, R. D. & Venugopal, P. 2002 Optimal large eddy simulation of turbulent channel flow based on direct numerical simulation statistical data. Phys. Fluids 14 (10), 36753691.Google Scholar
Voth, G. A., La Porta, A., Grawford, A. M., Alexander, J. & Bodenschatz, E. 2002 Measurements of particle accelerations in fully developed turbulence. J. Fluid Mech. 469, 121.Google Scholar
Voth, G. A., Satyanarayan, K. & Bodenschatz, E. 1998 Lagrangian acceleration measurements at large Reynolds numbers. Phys. Fluids 10, 2268.Google Scholar
Wang, Q. & Squires, K. D. 1996 Large eddy simulation of particle-laden turbulent channel flow. Phys. Fluids 8 (5), 12071223.CrossRefGoogle Scholar
Westbury, P. S., Dunn, D. C. & Morrison, J. F. 2004 Analysis of a stochastic backscatter model for the large-eddy simulation of wall-bonded flow. Eur. J. Mech. B 23, 737758.Google Scholar
Xu, H., Ouellette, N. T., Vincenzi, D. & Bodenschatz, E. 2007 Acceleration correlations and pressure structure functions in high-Reynolds number turbulence. Phys. Rev. Lett. 99, 204501.Google Scholar
Yeo, K., Kim, B.-G. & Lee, C. 2010 On the near-wall characteristics of acceleration in turbulence. J. Fluid Mech. 659, 405419.Google Scholar
Yeung, P. K., Pope, S. B., Lamorgese, A. G. & Donzis, D. A. 2006 Acceleration and dissipation statistics of numerically simulated isotropic turbulence. Phys. Fluids 18, 065103.Google Scholar
Zamansky, R., Vinkovic, I. & Gorokhovski, M. 2010 LES approach coupled with stochastic forcing of subgrid acceleration in a high Reynolds number channel flow. J. Turbul. 11 (30), 118.Google Scholar