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Subgrid-scale backscatter in reacting and inert supersonic hydrogen–air turbulent mixing layers

Published online by Cambridge University Press:  10 March 2014

J. O’Brien
Affiliation:
Center for Turbulence Research, Stanford University, Stanford, CA 94305-3024, USA
J. Urzay*
Affiliation:
Center for Turbulence Research, Stanford University, Stanford, CA 94305-3024, USA
M. Ihme
Affiliation:
Center for Turbulence Research, Stanford University, Stanford, CA 94305-3024, USA
P. Moin
Affiliation:
Center for Turbulence Research, Stanford University, Stanford, CA 94305-3024, USA
A. Saghafian
Affiliation:
Center for Turbulence Research, Stanford University, Stanford, CA 94305-3024, USA
*
Email address for correspondence: [email protected]

Abstract

This study addresses the dynamics of backscatter of kinetic energy in the context of large-eddy simulations (LES) of high-speed turbulent reacting flows. A priori analyses of direct numerical simulations (DNS) of reacting and inert supersonic, time-developing, hydrogen–air turbulent mixing layers with complex chemistry and multicomponent diffusion are conducted here in order to examine the effects of compressibility and combustion on subgrid-scale (SGS) backscatter of kinetic energy. The main characteristics of the aerothermochemical field in the mixing layer are outlined. A selfsimilar period is identified in which some of the turbulent quantities grow in a quasi-linear manner. A differential filter is applied to the DNS flow field to extract filtered quantities of relevance for the large-scale kinetic-energy budget. Spatiotemporal analyses of the flow-field statistics in the selfsimilar regime are performed, which reveal the presence of considerable amounts of SGS backscatter. The dilatation field becomes spatially intermittent as a result of the high-speed compressibility effect. In addition, the large-scale pressure-dilatation work is observed to be an essential mechanism for the local conversion of thermal and kinetic energies. A joint probability density function (PDF) of SGS dissipation and large-scale pressure-dilatation work is provided, which shows that backscatter occurs primarily in regions undergoing volumetric expansion; this implies the existence of an underlying physical mechanism that enhances the reverse energy cascade. Furthermore, effects of SGS backscatter on the Boussinesq eddy viscosity are studied, and a regime diagram demonstrating the relationship between the different energy-conversion modes and the sign of the eddy viscosity is provided along with a detailed budget of the volume fraction in each mode. A joint PDF of SGS dissipation and SGS dynamic-pressure dilatation work is calculated, which shows that high-speed compressibility effects lead to a decorrelation between SGS backscatter and negative eddy viscosities, which increases for increasingly large values of the SGS Mach number and filter width. Finally, it is found that the combustion dynamics have a marginal impact on the backscatter and flow-dilatation distributions, which are mainly dominated by the high-Mach-number effects.

Type
Papers
Copyright
© 2014 Cambridge University Press 

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References

Aluie, H., Li, S. & Li, H. 2012 Conservative cascade of kinetic energy in compressible turbulence. Astrophys. J. Lett. 751, 16.Google Scholar
Bose, S. T.2012 Explicitly-filtered large-eddy simulation. Ph.D. Thesis, Stanford University.Google Scholar
Brasseur, J. G.  & Wei, C. S. 1994 Interscale dynamics and local isotropy in high Reynolds number turbulence within triadic interactions. Phys. Fluids 6, 842870.Google Scholar
Brown, G. L.  & Roshko, A. 1974 On density effects and large structure in turbulent mixing layers. J. Fluid Mech. 64, 775816.Google Scholar
Carati, D., Ghosal, S. & Moin, P. 1995 On the representation of backscatter in dynamic localization models. Phys. Fluids 7, 606615.CrossRefGoogle Scholar
Cramer, M. S. 2013 Numerical estimates for the bulk viscosity of ideal gases. Phys. Fluids 24, 123.Google Scholar
Curran, E. T., Heiser, W. H. & Pratt, D. T. 1996 Fluid flow phenomena in SCRAMJET combustion systems. Ann. Rev. Fluid Mech. 28, 323360.CrossRefGoogle Scholar
Domaradzki, J. A., Liu, W. & Brachet, M. E. 1993 An analysis of subgrid-scale interactions in numerically simulated isotropic turbulence. Phys. Fluids 5, 17471759.CrossRefGoogle Scholar
Domaradzki, J. A. & Saiki, E. M. 1997 Backscatter models for large-eddy simulations. Theor. Comput. Fluid Dynam. 9, 7583.Google Scholar
Erlebacher, G., Hussaini, M. Y., Speziale, C. G. & Zang, T. A. 1992 Toward the large-eddy simulation of compressible turbulent flows. J. Fluid Mech. 238, 155185.Google Scholar
Fru, G., Janiga, G. & Thévenin, D. 2012 Impact of volume viscosity on the structure of turbulent premixed flames in the thin reaction zone regime. Flow Turbul. Combust. 88, 451478.Google Scholar
Germano, M. 1986 Differential filters for the large-eddy numerical simulation of turbulent flows. Phys. Fluids 6, 17551757.CrossRefGoogle Scholar
Germano, M., Piomelli, U., Moin, P. & Cabot, W. H. 1991 A dynamic subgrid-scale eddy viscosity model. Phys. Fluids 3, 17601765.Google Scholar
Ghosal, S., Lund, T. S., Moin, P. & Akselvoll, K. 1995 A dynamic localization model for large-eddy simulation of turbulent flows. J. Fluid Mech. 286, 229255.Google Scholar
Härtel, C., Kleiser, L., Unger, F. & Friedrich, R. 1994 Subgrid-scale energy transfer in the near-wall region of turbulent flows. Phys. Fluids 6, 31303143.Google Scholar
Hirschfelder, J., Curtiss, C. F. & Bird, R. B. 1954 Molecular theory of gases and liquids. John Wiley & Sons.Google Scholar
Hong, Z., Davidson, D. & Hanson, R. 2010 An improved ${\rm H}_{2}/{\rm O}_{2}$ mechanism based on recent shock tube/laser absorption measurements. Combust. Flame 158, 633644.CrossRefGoogle Scholar
Jaberi, F. J., Livescu, D. & Madnia, C. K. 2002 The effects of heat release on the energy exchange in reacting turbulent shear flow. J. Fluid Mech. 450, 3566.Google Scholar
Kerr, R. M., Domaradzki, J. A. & Barbier, G. 1994 Small scale properties of nonlinear interactions and subgrid-scale energy transfer in isotropic turbulence. Phys. Fluids 8, 197208.Google Scholar
Khalighi, Y., Nichols, J. W., Lele, S., Ham, F. & Moin, P. 2011 Unstructured large eddy simulations for prediction of noise issued from turbulent jets in various configurations. AIAA Paper 2011-2886.Google Scholar
Knaus, R. & Pantano, C. 2009 On the effect of heat release in turbulence spectra of non-premixed reacting shear layers. J. Fluid Mech. 626, 67109.CrossRefGoogle Scholar
Lee, S., Lele, S. & Moin, P. 1990 Eddy shocklets in decaying compressible turbulence. Phys. Fluids 3, 657664.Google Scholar
Leith, C. E. 1990 Stochastic backscatter in a subgrid-scale model: plane shear mixing layer. Phys. Fluids 3, 297300.Google Scholar
Lele, S. 1994 Compressibility effects on turbulence. Ann. Rev. Fluid Mech. 26, 211254.Google Scholar
Lesieur, M. & Métais, O. 1996 New trends in large-eddy simulations of turbulence. Ann. Rev. Fluid Mech. 28, 4582.Google Scholar
Libby, P. & Bray, K. N. C. 1981 Countergradient diffusion in premixed turbulent flames. AIAA J. 19, 205214.Google Scholar
Liñán, A. 1974 The asymptotic structure of counterflow diffusion flames. Acta Astronaut. 1, 10071039.Google Scholar
Liu, S., Meneveau, C. & Katz, J. 1994 On the properties of similarity subgrid-scale models as deduced from measurements in a turbulent jet. J. Fluid Mech. 275, 83119.Google Scholar
Lund, T. S., Ghosal, S. & Moin, P. 1993 Numerical experiments with highly variable eddy viscosity model. In Engineering Applications of Large Eddy Simulations vol. 162, pp. 711. FED ASME.Google Scholar
Luo, K. 1999 Combustion effects on turbulence in a partially premixed supersonic diffusion flame. Combust. Flame 119, 417435.Google Scholar
Mahle, I., Foysi, H., Sarkar, S. & Friedrich, R. 2007 On the turbulence structure in inert and reacting compressible mixing layers. J. Fluid Mech. 593, 171180.Google Scholar
Marsden, A. L., Vasilyev, O. V. & Moin, P. 2002 Construction of commutative filters for LES on unstructured meshes. J. Comput. Phys. 175 (2), 584603.CrossRefGoogle Scholar
Mason, P. J. & Thomson, D. J. 1992 Stochastic backscatter in large-eddy simulations of boundary layers. J. Fluid Mech. 242, 5178.Google Scholar
McBride, B. J., Gordon, S. & Reno, M. A. 2005 Coefficients for calculating thermodynamic and transport properties of individual species. NASA Tech. Memo. 4513.Google Scholar
Menevau, C. & Katz, J. 2000 Scale-invariance and turbulence models for LES. Ann. Rev. Fluid Mech. 32, 132.Google Scholar
Miller, R. S., Madnia, C. K. & Givi, P. 1994 Structure of a turbulent mixing layer. Combust. Sci. Technol. 99, 136.Google Scholar
Moin, P., Squires, K., Cabot, W. H. & Lee, S. 1991 A dynamic subgrid-scale model for compressible turbulence and scalar transport. Phys. Fluids 3, 17661771.Google Scholar
Moser, R. D. & Jiménez, J. 2000 Large-eddy simulations: where are we and what can we expect?. AIAA J. 38, 605613.Google Scholar
Pantano, C. & Sarkar, S. 2002 A study of compressibility effects in the high-speed turbulent shear layer using direct simulation. J. Fluid Mech. 451, 329371.Google Scholar
Pantano, C., Sarkar, S. & Williams, F. A. 2003 Mixing of a conserved scalar in a turbulent reacting shear layer. J. Fluid Mech. 481, 291328.Google Scholar
Papamoschou, D. & Roshko, A. 1988 The compressible turbulent shear layer: an experimental study. J. Fluid Mech. 197, 453477.Google Scholar
Peters, N. 2000 Turbulent Combustion. Cambridge University Press.Google Scholar
Piomelli, U., Cabot, W., Moin, P. & Lee, S. 1991 Subgrid-scale backscatter in turbulent and transitional flows. Phys. Fluids 3, 17661771.Google Scholar
Poinsot, T. & Lele, S. 1992 Boundary conditions for direct simulations of compressible viscous flows. J. Comput. Phys. 101, 104129.Google Scholar
Richardson, L. F. 1922 Weather Prediction by Numerical Process. Cambridge University Press.Google Scholar
Rogallo, R. S. & Moin, P. 1984 Numerical simulation of turbulent flows. Ann. Rev. Fluid Mech. 16, 99137.Google Scholar
Rogers, M. & Moser, R. D. 1994 Direct simulation of a selfimilar turbulent mixing layer. Phys. Fluids 25, 903923.Google Scholar
Saghafian, A.2013 High-fidelity simulations and modeling of compressible reacting flows. Ph.D. Thesis, Stanford University.Google Scholar
Samtaney, R., Pullin, D. I. & Kosović, B. 2001 Direct numerical simulation of decaying compressible turbulence and shocklet statistics. Phys. Fluids 13, 14151430.Google Scholar
Shi, Y., Xiao, Z., He, X. T., Wang, J., Yang, Y. & Chen, S. 2013 Cascade of kinetic energy in three-dimensional compressible turbulence. Phys. Rev. Lett. 110, 15.Google Scholar
Smagorinsky, J. 1963 General circulation experiments with the primitive equations: I. The basic experiment. Mon. Weath. Rev. 91, 99164.Google Scholar
Tao, B., Katz, J. & Meneveau, C. 2002 Statistical geometry of subgrid-scale stresses determined from holographic particle image velocimetry measurements. J. Fluid Mech. 457, 3578.Google Scholar
Takagi, T., Shin, H. D. & Ishio, A. 1980 Local laminarization of turbulent diffusion flames. Combust. Flame 37, 163170.Google Scholar
Vreman, B., Geurts, B. & Kuerten, H. 1995a A priori tests of large eddy simulation of the compressible plane mixing layer. J. Engng. Maths. 54 (3), 299327.Google Scholar
Vreman, B., Geurts, B. & Kuerten, H. 1995b Subgrid-modelling in LES of compressible flow. Appl. Sci. Res. 54 (3), 191203.CrossRefGoogle Scholar
Vreman, A. W., Sandham, N. D. & Luo, K. H. 1996 Compressible mixing layer growth rate and turbulence characteristics. J. Fluid Mech. 320, 235258.Google Scholar
Vreman, A. W. 2004 An eddy-viscosity subgrid-scale model for turbulent shear flow: algebraic theory and applications. Phys. Fluids 16 (10), 112.Google Scholar
Williams, F. A. 1985 Combustion Theory. Benjamin Cummings.Google Scholar
You, D., Ham, F. & Moin, P. 2008 Discrete conservation principles in large-eddy simulation with application to separation control over an airfoil. Phys. Fluids 20, 111.Google Scholar