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Axisymmetric description of the scale-by-scale scalar transport

Axisymmetric description of the scale-by-scale scalar transport. Luminita Danaila. Context: ANR ‘ANISO’: F. Godeferd, C. Cambon, J.B. Flor ANR ‘Micromixing’: B. Renou, J.F. Krawczynski , G. Boutin, F. Thiesset CORIA, Saint-Etienne-du-Rouvray, FRANCE.

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Axisymmetric description of the scale-by-scale scalar transport

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  1. Axisymmetric description of the scale-by-scale scalar transport Luminita Danaila Context: ANR ‘ANISO’: F. Godeferd, C. Cambon, J.B. Flor ANR ‘Micromixing’: B. Renou, J.F. Krawczynski,G. Boutin, F. Thiesset CORIA, Saint-Etienne-du-Rouvray, FRANCE

  2. OUTLINE I. Context, previous work and motivation II. Analytical development III. Validation with experimental data V. Conclusions

  3. I. Context and motivation FLOW Re Kolmogorov : Local isotropy Universality Real space: A simple analytical plateform: relation between the second-and the third-order moments at a scale r  energy flux at a scale r

  4. I. Context and motivation Scale-by-scale energy budget: Kolmogorov (1941) Non-universality for moderate Reynolds numbers Antonia & Burattini, 2006

  5. For different REAL flows (moderate Reynolds, locally isotropic or anisotropic …) Necessity to account for explicitly the non-negligible correlation between large-and small scales I. Context and motivation

  6. I. Context and motivation Method: Navier-Stokes in 2 space points: Increments: Isotropy (local) and integration with respect to r

  7. I. Context and motivation Kolmogorov, 1941Saffman 1968, Danaila et al. 1999, Lindborg 1999 Finite Reynolds numbers- flows: Grid turbulence, round jet, channel flow (axis, near wall) ... Conclusion: Energy transferred at a scale r= turbulent diffusion + molecular effects+ large-scale effects: shear, decay, mean temperature gradient …

  8. I. Context and motivation Similar questions hold for scalars and turbulent kinetic energy Kolmogorov, 1941 Yaglom, 1949 Danaila et al. 1999 R.A. Antonia et al. 1997 Danaila et al., 2004 Burattini et al., 2005 Real- Finite Reynolds numbers- flows: Slightly heated grid turbulence, grid turbulence with a Mean scalar gradient .. Same conclusion : Energy transferred at a scale r …large-scale effects

  9. II. Analytical development Shortcome: local isotropy was supposed … Question: which is the anisotropic/axisymmetric form of ? Scalar equation  simpler development Note: The axisymmetric form of Kolmogorov equation Chandrasekar 1950, Lindborg 1996, Antonia et al. 2000, Ould-Rouis 2001 …. Problem: a large number of scalars which are difficult/impossible to be determined from experiments

  10. II. Analytical development From: (1) Several ways: isotropy  dependence on r integration over a sphere of radius r dependence on r …. All the terms in Eq. (1) depend on 2 variables

  11. II. Analytical development Development similar to Shivamoggi and Antonia, (Fluid Dyn. Res., 2000) Chandrasekar, 1950 Measurable: and Injection: decay, production..

  12. II. Analytical development With: and Eq. (2) C. Cambon, L. Danaila, F. Godeferd, Y. Gagne and J. Scott, in preparation

  13. III. Validation with experimental data: EXPERIMENTAL SET-UP* • Volume PaSR: V = 11116 cm3 • Injection velocity: UJ = 4.5 - 47 m/s • Return flow= porous top/bottom plates • Residence time: tR = 8 -46 ms Reynolds number 60 Rl 1000 (center) *Prof. P.E. Dimotakis of Caltech was responsible for the conceptual and detailed design of the PaSR and contributed to the initial experiments.

  14. z y x III. Validation with experimental data A forced box turbulence Injection zone = impinging jets « Mixing » zone = stagnation zone Return flow (top/bottom porous)

  15. III. Validation with experimental data Energy Isotropy? II I

  16. III. Validation with experimental data Third order ‘classical’ Structure functions : Selection of one particular direction JETS The sign changes at Large scales (inhomogeneity)

  17. III. Validation with experimental data r V H r H V

  18. III. Validation with experimental data Eq. (3)

  19. III. Validation with experimental data

  20. IV. Conclusions • Theory: • -Scale-by-scale energy budget equation for kinetic energy (scalar) in axisymmetric • turbulence (axis of a round jet, axis of two opposed jets ..) • -Measured quantities: u and v (components perpendicular and parallel to ) • -all the terms in Eq. (2) can be determined experimentally • The flow: • Pairs of impinging jets; Return flow by top/bottom porous locally axisymmetric flow • Results : • - better agreement with asymptotic predictions (high Reynolds, anisotropic flows) along the direction normal to the axisymmetry axis (homogeneous plane) than along the direction parallel to

  21. IV. Description of the scalar mixing: fluctuating field Instantaneous fields of the mixing fraction z H/D=3 H/D=5 Large pannel of structures Large-scale instabilities (jets flutter) Mechanisms controlling the mixing?

  22. 26252 8 injection conditions 6089 III. Validation with experimental data Re 104 2 Geometries: H/D=3 H/D=5

  23. The other tests 2-rd order SF with the Kolmogorov constant Normalized dissipation which L? Attention to initial conditions versus universality .. However, a reliable test for The most reliable test is the 1—point energy budget equation, when the pressure-related terms might be neglected (point II). III. DESCRIPTION of the FLOW: fluctuating field; PIV for determining small scale properties

  24. III. Validation with experimental data Velocity field: 1) Particle velocimetry • Resolution and noise limitations • PIV resolution linked to size of interrogation/correlation window, e.g., 16, 32, and 64 pix2, and processing algorithm choices • Does not resolve small scales: the smallest 100% =1.7 mm • Problem to estimate energy dissipation directly • Towards adaptive/optimal vector processing/filtering 2) LDV in (1 point and) 2 points • Simultaneous measurements of One velocity component in two points of the space: spatial resolution 200 * 50 microns; sampling frequency= 20 kHZ Scalar field: PLIF on acetone Small-scale limitations set by spatial resolution (pixel/laser-sheet size) The smallest resolved scale 100% =0.7 mm Signal-to-noise ratio per pixel • Adaptive/optimal image processing/filtering

  25. III. Validation with experimental data Third order ‘classical’ Structure functions JETS

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