Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T18:51:20.712Z Has data issue: false hasContentIssue false

Turbulence and energy budget in a self-preserving round jet: direct evaluation using large eddy simulation

Published online by Cambridge University Press:  25 May 2009

C. BOGEY*
Affiliation:
Laboratoire de Mécanique des Fluides et d'Acoustique, UMR CNRS 5509, Ecole Centrale de Lyon, 69134 Ecully Cedex, France
C. BAILLY
Affiliation:
Laboratoire de Mécanique des Fluides et d'Acoustique, UMR CNRS 5509, Ecole Centrale de Lyon, 69134 Ecully Cedex, France Institut Universitaire de France, 103 Boulevard Saint-Michel, 75005 Paris, France
*
Email address for correspondence: [email protected]

Abstract

An axisymmetric jet at a diameter-based Reynolds number of 1.1 × 104 is computed by a large eddy simulation (LES) in order to investigate its self-similarity region. The LES combines low-dissipation numerical schemes and explicit filtering of the flow variables to relax energy through the smaller scales discretized. The computational domain extends up to 150 jet radii in the downstream direction, which is found to be large enough to discretize a part of this region. Turbulence in the self-preserving jet is characterized by evaluating explicitly from the LES fields the second- and third-order moments of velocity, the pressure–velocity correlations as well as the budgets for the turbulent kinetic energy and for its components. Reference solutions are thus obtained. They agree well with the experimental data given by Panchapakesan & Lumley (J. Fluid Mech., vol. 246, 1963, p. 197) for a jet at the same Reynolds number. The distance required to achieve self-similarity in the LES, around 120 radii from the inflow, is particularly similar to that in the experiment. The discrepancies observed with respect to the data provided by Panchapakesan & Lumley and by Hussein, Capp & George (J. Fluid Mech., vol. 258, 1994, p. 31) for a jet at a higher Reynolds number, specially regarding the turbulence diffusion and the dissipation, are discussed. They appear largely resulting from the approximations made in the experiments to estimate the quantities that cannot be measured with accuracy. The role of the pressure terms in the energy redistribution is also clarified by the LES. Moreover, the turbulent energy budget is calculated in the jet from an equation derived from the filtered compressible Navier–Stokes equations, which includes the dissipation due to the explicit filtering. This has allowed us to assess the behaviour of the LES approach based on relaxation filtering (LES-RF) from the contributions of filtering and viscosity to energy dissipation. The filtering activity is particularly shown to adjust by itself to the grid and flow properties.

Type
Papers
Copyright
Copyright © Cambridge University Press 2009

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.)

References

REFERENCES

Berland, J., Bogey, C. & Bailly, C. 2007 Numerical study of screech generation in a planar supersonic jet. Phys. Fluids 19 (7), 075105.CrossRefGoogle Scholar
Boersma, B. J., Brethouwer, G. & Nieuwstadt, F. T. M. 1998 A numerical investigation on the effect of the inflow conditions on the self-similar region of a round jet. Phys. Fluids 10 (4), 899909.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2002 Three-dimensional non-reflective boundary conditions for acoustic simulations: far field formulation and validation test cases. Acta Acust. 88 (4), 463471.Google Scholar
Bogey, C. & Bailly, C. 2004 A family of low dispersive and low dissipative explicit schemes for flow and noise computations. J. Comput. Phys. 194 (1), 194214.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2005 a Decrease of the effective Reynolds number with eddy-viscosity subgrid-scale modeling. AIAA J. 43 (2), 437439.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2005 b Effects of inflow conditions and forcing on a Mach 0.9 jet and its radiated noise. AIAA J. 43 (5), 10001007.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2006 a Investigation of downstream and sideline subsonic jet noise using Large Eddy Simulation. Theor. Comput. Fluid Dyn. 20 (1), 2340.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2006 b Large Eddy Simulations of round free jets using explicit filtering with/without dynamic Smagorinsky model. Intl J. Heat Fluid Flow 27 (4), 603610.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2006 c Large Eddy Simulations of transitional round jets: influence of the Reynolds number on flow development and energy dissipation. Phys. Fluids 18 (6), 065101.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2006 d Computation of a high Reynolds number jet and its radiated noise using large eddy simulation based on explicit filtering. Comput. Fluids 35 (10), 13441358.CrossRefGoogle Scholar
Bogey, C. & Bailly, C. 2007 An analysis of the correlations between the turbulent flow and the sound pressure field of subsonic jets. J. Fluid Mech. 583, 7197.CrossRefGoogle Scholar
Bogey, C., Bailly, C. & Juvé, D. 2003 Noise investigation of a high subsonic, moderate Reynolds number jet using a compressible LES. Theor. Comput. Fluid Dyn. 16 (4), 273297.CrossRefGoogle Scholar
Bradbury, L. J. S. 1965 The structure of a self-preserving turbulent plane jet. J. Fluid Mech. 23 (1), 3164.CrossRefGoogle Scholar
Burattini, P., Antonia, R. A. & Danaila, L. 2005 Similarity in the far field of a turbulent round jet. Phys. Fluids 17 (2), 025101.CrossRefGoogle Scholar
Dantinne, G., Jeanmart, H., Winckelmans, G. S., Legat, V. & Carati, D. 1998 Hyperviscosity and vorticity-based models for subgrid scale modeling. Appl. Sci. Res. 59, 409420.CrossRefGoogle Scholar
Davies, P. O. A. L., Fisher, M. J. & Barratt, M. J. 1962 The characteristics of the turbulence in the mixing region of a round jet. J. Fluid Mech. 15, 337367.CrossRefGoogle Scholar
Dejoan, A. & Leschziner, M. A. 2005 Large eddy simulation of a plane turbulent wall jet. Phys. Fluids A 17 (2), 025102.CrossRefGoogle Scholar
Deo, R. C., Mi, J. & Nathan, G. J. (2008) The influence of Reynolds number on a plane jet. Phys. Fluids 20, 075108.CrossRefGoogle Scholar
Domaradzki, J. A. & Adams, N. A. 2002 Direct modeling of subgrid scales of turbulence in Large-Eddy Simulation. J. Turbul. 3, 119.CrossRefGoogle Scholar
Domaradzki, J. A., Xiao, Z. & Smolarkiewicz, P. K. 2003 Effective eddy viscosities in implicit large eddy simulations of turbulent flows. Phys. Fluids 15 (12), 38903893.CrossRefGoogle Scholar
Domaradzki, J. A. & Yee, P. P. 2000 The subgrid-scale estimation model for high Reynolds number turbulence. Phys. Fluids 12 (1), 193196.CrossRefGoogle Scholar
Garnier, E., Mossi, M., Sagaut, P., Comte, P. & Deville, M. 1999 On the use of shock-capturing schemes for large-eddy simulation. J. Comput. Phys. 153 (2), 273311.CrossRefGoogle Scholar
George, W. K. & Hussein, H. J. 1991 Locally axisymmetric turbulence. J. Fluid Mech. 233, 123.CrossRefGoogle Scholar
Geurts, B. J. 2004 Elements of Direct and Large-Eddy Simulation. Edwards.Google Scholar
Geurts, B. J. & Fröhlich, J. 2002 A framework for predicting accuracy limitations in large-eddy simulations. Phys. Fluids 14 (6), 4144.CrossRefGoogle Scholar
Grinstein, F. F. & Fureby, C. 2002 Recent progress on MILES for high Reynolds number flows. J. Fluid Engng 124, 848861.CrossRefGoogle Scholar
Gutmark, E. & Wygnanski, I. 1976 The planar turbulent jet. J. Fluid Mech. 73, 465495.CrossRefGoogle Scholar
Hickel, S., Adams, N. A. & Domaradzki, J. A. 2006 An adaptative local deconvolution method for implicit LES. J. Comput. Phys. 213 (2), 413436.CrossRefGoogle Scholar
Hussein, H. J., Capp, S. P. & George, W. K. 1994 Velocity measurements in a high-Reynolds-number, momentum-conserving, axisymmetric, turbulent jet. J. Fluid Mech. 258, 3175.CrossRefGoogle Scholar
Le Ribault, C., Sarkar, S. & Stanley, S. 1999 Large eddy simulation of a plane jet. Phys. Fluids 11 (10), 30693083.CrossRefGoogle Scholar
Lesieur, M. & Métais, O. 1996 New trends in large-eddy simulations of turbulence. Annu. Rev. Fluid Mech. 28, 4582.CrossRefGoogle Scholar
Lumley, J. L. 1978 Computational modelling of turbulent flows. Adv. Appl. Mech. 18, 123126.CrossRefGoogle Scholar
Mansour, N. N., Kim, J. & Moin, P. 1988 Reynolds-stress and dissipation-rate budgets in a turbulent channel flow. J. Fluid Mech. 194, 1544.CrossRefGoogle Scholar
Marsden, O., Bogey, C. & Bailly, C. 2008 Direct noise computation of the turbulent flow around a zero-incidence airfoil. AIAA J. 46 (4), 874883.CrossRefGoogle Scholar
Mathew, J., Lechner, R., Foysi, H., Sesterhenn, J. & Friedrich, R. 2003 An explicit filtering method for large eddy simulation of compressible flows. Phys. Fluids 15 (8), 22792289.CrossRefGoogle Scholar
Meneveau, C. & Katz, J. 2000 Scale-invariance and turbulence models for large-eddy simulation. Annu. Rev. Fluid Mech. 32, 132.CrossRefGoogle Scholar
Panchapakesan, N. R. & Lumley, J. L. 1993 Turbulence measurements in axisymmetric jets of air and helium. Part I. Air jet. J. Fluid Mech. 246, 197223.CrossRefGoogle Scholar
Pasquetti, R. 2006 Spectral vanishing viscosity method for large eddy simulation of turbulent flows. J. Sci. Comput. 27, 365375.CrossRefGoogle Scholar
Pope, S. B. 2000 Turbulent Flows. Cambridge University Press.CrossRefGoogle Scholar
Ricou, F. P. & Spalding, D. B. 1961 Measurements of entrainment by axisymmetrical turbulent jets. J. Fluid Mech. 11, 2132.CrossRefGoogle Scholar
Rizzetta, D. P., Visbal, M. R. & Blaisdell, G. A. 2003 A time-implicit high-order compact differencing and filtering scheme for large-eddy simulation. Intl J. Num. Methods Fluids 42 (6), 665693.CrossRefGoogle Scholar
Sagaut, P. 2005 Large-Eddy Simulation for Incompressible Flows: An Introduction, 3rd ed. Springer.Google Scholar
Sami, S. 1967 Balance of turbulence energy in the region of jet-flow establishment. J. Fluid Mech. 29 (1), 8192.CrossRefGoogle Scholar
Sami, S., Carmody, T. & Rouse, H. 1967 Jet diffusion in the region of flow establishment. J. Fluid Mech. 27 (2), 231252.CrossRefGoogle Scholar
Schlatter, P., Stolz, S. & Kleiser, L. 2004 LES of transitional flows using the approximate deconvolution model. Intl J. Heat Fluid Flow 25 (3), 549558.CrossRefGoogle Scholar
Schlatter, P., Stolz, S. & Kleiser, L. 2006 Analysis of the SGS energy budget for deconvolution- and relaxation-based models in channel flow. In Direct and Large-Eddy Simulation VI (ed. Lamballais, E., Friedrich, R., Geurts, B. J. & Métais, O.), pp. 135142. Springer.CrossRefGoogle Scholar
Stanley, S. A., Sarkar, S. & Mellado, J. P. 2002 A study of the flowfield evolution and mixing in a planar turbulent jet using direct numerical simulation. J. Fluid Mech. 450, 377407.CrossRefGoogle Scholar
Stolz, S., Adams, N. A. & Kleiser, L. 2001 An approximate deconvolution model for Large-Eddy Simulation of incompressible flows. Phys. Fluids 13 (4), 9971015.CrossRefGoogle Scholar
Tam, C. K. W. & Webb, J. C. 1993 Dispersion-relation-preserving finite difference schemes for computational acoustics. J. Comput. Phys. 107 (2), 262281.CrossRefGoogle Scholar
Uddin, M. & Pollard, A. 2007 Self-similarity of coflowing jets: the virtual origin. Phys. Fluids 19, 068103-1068103-4.CrossRefGoogle Scholar
Vreman, B., Geurts, B. & Kuerten, H. 1995 Subgrid-modeling in LES of compressible flow. Appl. Sci. Res. 54, 191203.CrossRefGoogle Scholar
Vreman, B., Geurts, B. & Kuerten, H. 1997 Large-eddy simulation of the turbulent mixing layer. J. Fluid Mech. 339, 357390.CrossRefGoogle Scholar
Weisgraber, T. H. & Liepmann, D. 1998 Turbulent structure during transition to self-similarity in a round jet. Exp. Fluids 24, 210224.CrossRefGoogle Scholar
Wygnanski, I. & Fiedler, H. 1969 Some measurements in the self-preserving jet. J. Fluid Mech. 38 (3), 577612.CrossRefGoogle Scholar