Skip to main content
Log in

On Fokker–Planck Equations for Turbulent Reacting Flows. Part 1. Probability Density Function for Reynolds-Averaged Navier–Stokes Equations

  • Published:
Flow, Turbulence and Combustion Aims and scope Submit manuscript

Abstract

The accurate treatment of finite-rate chemistry is possible by the application of stochastic turbulence models which generalize Reynolds-averaged Navier–Stokes equations. Usually, one considers linear stochastic equations. In this way, fluctuations are generated by uncorrelated forces and relax with a frequency that is independent of the actual fluctuation. It has been proved that such linear equations are well appropriate to simulate near-equilibrium flows. However, the inapplicability or unfeasibility of other methods also results in a need for stochastic methods for more complex flow simulations. Their construction requires an extension of the simple mechanism of linear stochastic equations. Two ways to perform this are investigated here. The first way is the construction of a stochastic model for velocities where the relaxation frequency depends on the actual fluctuation. This is a requirement to involve relevant mixing variations due to large-scale flow structures. The stochastic model developed is applied to the simulation of convective boundary layer turbulence. Comparisons with the results of measurements provide evidence for its good performance and the advantages compared to existing methods. The second way presented here is the construction of scalar equations which involve memory effects regarding to both the stochastic forcing and relaxation of fluctuations. This allows to overcome shortcomings of existing stochastic methods. The model predictions are shown to be in excellent agreement with the results of the direct numerical simulation of scalar mixing in stationary, homogeneous and isotropic turbulence. The consideration of memory effects is found to be essential to simulate correctly the evolution of scalar fields within the first stage of mixing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Baldyga, J. and Bourne, J.R., Turbulent Mixing and Chemical Reactions. John Wiley & Sons, Chichester (1999).

    Google Scholar 

  2. Canuto, V.M., Turbulent convection with overshooting: Reynolds stress approach. Astrophys. J. 392 (1992) 218–232.

    Google Scholar 

  3. Canuto, V.M., Minotti, F., Ronchi, C., Ypma, R.M. and Zeman, O., Second-order closure PBL model with new third-order moments: Comparison with LES data. J. Atmos. Sci. 51 (1994) 1605–1618.

    Google Scholar 

  4. Craft, T.J., Kidger, J.W. and Launder, B.E., Importance of third-moment modeling in horizontal, stably-stratified flows. In: Proceedings of the 11th Symposium on Turbulent Shear Flows, Grenoble, France (1997) pp. 2013–2018.

  5. Craft, T.J., Developments in a low-Reynolds number second-moment closure and its application to separating and reattaching flows. Internat. J. Heat Fluid Flow 19 (1998) 541–548.

    Google Scholar 

  6. Dopazo, C., Recent developments in PDF methods. In: Libby, P.A. and Williams, F.A. (eds.), Turbulent Reacting Flows. Academic Press, San Diego, CA (1994) pp. 375–474.

    Google Scholar 

  7. Du, S., Wilson, J.D. and Yee, E., Probability density functions for velocity in the convective boundary layer, and implied trajectory models. Atmos. Environ. 28 (1994) 1211–1217.

    Google Scholar 

  8. Du, S., Wilson, J.D. and Yee, E., On the moments approximation method for constructing a Lagrangian stochastic model. Bound.-Layer Meteorol. 40 (1994) 273–292.

    Google Scholar 

  9. Du, S., Sawford, B.L., Wilson, J.D. and Wilson, D.J., Estimation of the Kolmogorov constant (C0) for the Lagrangian structure function, using a second-order Lagrangian model of grid turbulence. Phys. Fluids 7 (1995) 3083–3090.

    Google Scholar 

  10. Durbin, P.A. and Speziale, C.G., Realizability of second-moment closure via stochastic analysis. J. Fluid Mech. 280 (1994) 395–407.

    Google Scholar 

  11. Eswaran, V. and Pope, S.B., Direct numerical simulations of the turbulent mixing of a passive scalar. Phys. Fluids 31 (1988) 506–520.

    Google Scholar 

  12. Fox, R.O., The Fokker-Planck closure for turbulent molecular mixing: Passive scalars. Phys. Fluids A 4 (1992) 1230–1244.

    Google Scholar 

  13. Fox, R.O., Improved Fokker-Planck model for the joint scalar, scalar gradient PDF. Phys. Fluids 6 (1994) 334–348.

    Google Scholar 

  14. Fox, R.O., Computational methods for turbulent reacting flows in the chemical process industry. Rev. Inst. Français du Pétrole 51 (1996) 215–243.

    Google Scholar 

  15. Fox, R.O., On velocity-conditioned scalar mixing in homogeneous turbulence. Phys. Fluids 8 (1996) 2678–2691.

    Google Scholar 

  16. Gardiner, C.W., Handbook of Statistical Methods. Springer-Verlag, Berlin (1983).

    Google Scholar 

  17. Grabert, H., Projection Operator Technique in Nonequilibrium Statistical Mechanics. Springer-Verlag, Berlin (1982).

    Google Scholar 

  18. Heinz, S., Nonlinear Lagrangian equations for turbulent motion and buoyancy in inhomogeneous flows. Phys. Fluids 9 (1997) 703–716.

    Google Scholar 

  19. Heinz, S., Time scales of stratified turbulent flows and relations between second-order closure parameters and flow numbers. Phys. Fluids 10 (1998) 958–973.

    Google Scholar 

  20. Heinz, S., Connections between Lagrangian stochastic models and the closure theory of turbulence for stratified flows. Internat. J. Heat Fluid Flow 19 (1998) 193–200.

    Google Scholar 

  21. Heinz, S. and van Dop, H., Buoyant plume rise described by a Lagrangian turbulence model. Atmos. Environ. 33 (1999) 2031–2043.

    Google Scholar 

  22. Heinz, S., On Fokker-Planck equations for turbulent reacting flows. Part 2. Filter density function for large eddy simulation. Flow, Turb. Combust. 70 (2003) 153–181.

    Google Scholar 

  23. Heinz, S., Statistical Mechanics of Turbulent Flows. Springer-Verlag, Berlin (2003).

    Google Scholar 

  24. Ilyushin, B.B. and Kurbatski, A.F., Modeling of turbulent transport in PBL with third-order moments. In: Proceedings of the 11th Symposium on Turbulent Shear Flows, Grenoble, France (1997) pp. 2019–2024.

  25. Juneja, A. and Pope, S.B., A DNS study of turbulent mixing of two passive scalars. Phys. Fluids 8 (1996) 2161–2184.

    Google Scholar 

  26. Kassinos, S., Reynolds, W.C. and Rogers, M.M., One-point turbulence structure tensors. J. Fluid Mech. 428 (2001) 213–248.

    Google Scholar 

  27. Kawamura, H., Sasaki, J. and Kobayashi, K., Budget and modeling of triple moment velocity correlations in a turbulent channel flow based on DNS. In: Proccedings of the 10th Symposium on Turbulent Shear Flows, Pennsylvania State University (1995) pp. 2613–2618.

  28. Kerstein, A.R., Linear-eddy modeling of turbulent transport. II: Application to shear layer mixing. Combust. Flame 75 (1989) 397–413.

    Google Scholar 

  29. Kerstein, A.R., Linear-eddy modeling of turbulent transport. Part 3. Mixing and differential molecular diffusion in round jets. J. Fluid Mech. 216 (1990) 411–435.

    Google Scholar 

  30. Kerstein, A.R., One-dimensional turbulence: Model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows. J. Fluid Mech. 392 (1999) 277–334.

    Google Scholar 

  31. Kuo, K.K., Principles of Combustion. Wiley Interscience, New York (1986).

    Google Scholar 

  32. Launder, B.E., Phenomenological modeling: Present and future. In: Lumley, J.L. (ed.), Wither Turbulence? Turbulence at the Crossroads. Springer-Verlag, Berlin (1990) pp. 439–485.

    Google Scholar 

  33. Lindenberg, K. and West, J.W., The Nonequilibrium Statistical Mechanics of Open and Closed Systems. VHC Publishers, New York (1990).

    Google Scholar 

  34. Luhar, A.K., Hibberd, M.F. and Hurley, P.J., Comparison of closure schemes used to specify the velocity PDF in Lagrangian stochastic dispersion models for convective conditions. Atmos. Environ. 30 (1996) 1407–1418.

    Google Scholar 

  35. Peters, N., Turbulent Combustion. Cambridge University Press, Cambridge (2001).

    Google Scholar 

  36. Pope, S.B., Probability distributions of scalars in turbulent shear flow. In: Bradbury, L.S.S., Durst, F., Launder, B.E., Schmidt, F.W. and Whitelaw, J.H. (eds.), Turbulent Shear Flows 2. Springer-Verlag, Berlin (1980) pp. 7–16.

    Google Scholar 

  37. Pope, S.B., PDF methods for turbulent reactive flows. Prog. Energy Combust. Sci. 11 (1985) 119–192.

    Google Scholar 

  38. Pope, S.B., On the relationship between stochastic Lagrangian models of turbulence and second-moment closures. Phys. Fluids 6 (1994) 973–985.

    Google Scholar 

  39. Pope, S.B., The vanishing effect of molecular diffusivity on turbulent dispersion: Implications for turbulent mixing and the scalar flux. J. Fluid Mech. 359 (1998) 299–312.

    Google Scholar 

  40. Pope, S.B., Turbulent Flows. Cambridge University Press, Cambridge (2000).

    Google Scholar 

  41. Risken, H., The Fokker-Planck Equation. Springer-Verlag, Berlin (1984).

    Google Scholar 

  42. Sawford, B.L., Recent developments in the Lagrangian stochastic theory of turbulent dispersion. Bound.-Layer Meteorol. 62 (1993) 197–215.

    Google Scholar 

  43. Sawford, B.L., Rotation of Lagrangian stochastic models of turbulent dispersion. Bound.-Layer Meteorol. 93 (1999) 411–424.

    Google Scholar 

  44. Speziale, C.G. and Xu, X., Towards the development of second-order closure models for nonequilibrium turbulent flows. Internat. J. Heat Fluid Flow 17 (1996) 238–244.

    Google Scholar 

  45. Thomson, D.J., Criteria for the selection of stochastic models of particle trajectories in turbulent flows. J. Fluid Mech. 180 (1987) 529–556.

    Google Scholar 

  46. Thomson, D.J. and Montgomery, M.R., Reflection boundary conditions for random walk models of dispersion in non-Gaussian turbulence. Atmos. Environ. 28 (1994) 1981–1987.

    Google Scholar 

  47. Valiño, L. and Dopazo, C., A binomial Langevin model for turbulent mixing. Phys. Fluids A 3 (1991) 3034–3037.

    Google Scholar 

  48. Van Dop, H., Nieuwstadt, F.T.M. and Hunt, J.C.R., Random walk models for particle displacements in inhomogeneous unsteady turbulent flows. Phys. Fluids 28 (1985) 1639–1653.

    Google Scholar 

  49. Wilson, J.D. and Sawford, B.L., Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Bound.-Layer Meteorol. 78 (1996) 191–210.

    Google Scholar 

  50. Zubarev, D., Morozov, V. and Röpke, G., Statistical Mechanics of Nonequilibrium Processes. Vol. 1: Basic Concepts, Kinetic Theory. Akademie Verlag (VCH Publishers), Berlin (1996).

    Google Scholar 

  51. Zubarev, D., Morozov, V. and Röpke, G., Statistical Mechanics of Nonequilibrium Processes. Vol. 2: Relaxation and Hydrodynamic Processes. Akademie Verlag (VCH Publishers), Berlin (1997).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heinz, S. On Fokker–Planck Equations for Turbulent Reacting Flows. Part 1. Probability Density Function for Reynolds-Averaged Navier–Stokes Equations. Flow, Turbulence and Combustion 70, 115–152 (2003). https://doi.org/10.1023/B:APPL.0000004933.17800.46

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:APPL.0000004933.17800.46

Navigation