Effects of initial mixture fraction on the development of a turbulent reactive mixing layer

2011 ◽  
Vol 12 ◽  
pp. N33
Author(s):  
Mani Fathali ◽  
Bamdad Lessani
Keyword(s):  
Mixture Fraction ◽  
Mixing Layer ◽  
Initial Mixture ◽  
Reactive Mixing

Ignition of a reactive mixing layer

1999 ◽  
Vol 96 (6) ◽  
pp. 1016-1021 ◽  
Author(s):  
C. Nicoli ◽  
P. Haldenwang ◽  
B. Denet
Keyword(s):  
Mixing Layer ◽  
Reactive Mixing

Large-scale structure and entrainment in the supersonic mixing layer

1995 ◽  
Vol 284 ◽  
pp. 171-216 ◽  
Author(s):  
N. T. Clemens ◽  
M. G. Mungal
Keyword(s):  
Mach Number ◽  
High Speed ◽  
Large Scale ◽  
Mixture Fraction ◽  
Mixing Layer ◽  
Mie Scattering ◽  
Planar Laser ◽  
The Cross ◽  
Convective Mach Number ◽  
Stream Direction

Experiments were conducted in a two-stream planar mixing layer at convective Mach numbers,Mc, of 0.28, 0.42, 0.50, 0.62 and 0.79. Planar laser Mie scattering (PLMS) from a condensed alcohol fog and planar laser-induced fluorescence (PLIF) of nitric oxide were used for flow visualization in the side, plan and end views. The PLIF signals were also used to characterize the turbulent mixture fraction fluctuations.Visualizations using PLMS indicate a transition in the turbulent structure from quasi-two-dimensionality at low convective Mach number, to more random three-dimensionality for$M_c\geqslant 0.62$. A transition is also observed in the core and braid regions of the spanwise rollers as the convective Mach number increases from 0.28 to 0.62. A change in the entrainment mechanism with increasing compressibility is also indicated by signal intensity profiles and perspective views of the PLMS and PLIF images. These show that atMc= 0.28 the instantaneous mixture fraction field typically exhibits a gradient in the streamwise direction, but is more uniform in the cross-stream direction. AtMc= 0.62 and 0.79, however, the mixture fraction field is more streamwise uniform and with a gradient in the cross-stream direction. This change in the composition of the structures is indicative of different entrainment motions at the different compressibility conditions. The statistical results are consistent with the qualitative observations and suggest that compressibility acts to reduce the magnitude of the mixture fraction fluctuations, particularly on the high-speed edge of the layer.

Frequency spectra of reactant fluctuations in turbulent flows

1993 ◽  
Vol 246 ◽  
pp. 489-502 ◽  
Author(s):  
George Kosály
Keyword(s):  
Turbulent Flows ◽  
Cross Correlation ◽  
Mixture Fraction ◽  
Spectral Characteristics ◽  
Mixing Layer ◽  
Correlation Coefficients ◽  
Frequency Spectra ◽  
Fast Chemistry ◽  
Limiting Values ◽  
Intermediate Rate

Bilger, Saetran & Krishnamoorthy (1991) give measured values of the variance, cross-correlation coefficient, autospectra, coherence and phase shift of the reactant concentration fluctuations for an irreversible second-order reaction in an incompressible turbulent scalar mixing layer. The present paper approaches the interpretation of the measured data by evaluating the above quantities in the frozen (slow) and equilibrium (fast) chemistry limits. We assume that the limiting values bracket the corresponding intermediate rate data.The analysis leads to values that correspond with the measured variances and correlation coefficients. The paper offers simple procedures for experimenters to evaluate the fast chemistry limit of the spectral characteristics from the measured mixture fraction fluctuations. The investigation of the limiting spectra suggests that, in the frequency region considered in the Bilger et al. measurements, the shape of the autospectrum is quite insensitive to the chemistry rate. The cross-spectrum is much more sensitive to the chemistry than the autospectrum. The analysis predicts correctly that the coherence decreases with increasing frequency while the phase stays equal to π until the decrease of the coherence leads to indeterminate phase results.

Compressibility effects in a turbulent annular mixing layer. Part 2. Mixing of a passive scalar

2000 ◽  
Vol 421 ◽  
pp. 269-292 ◽  
Author(s):  
JONATHAN B. FREUND ◽  
PARVIZ MOIN ◽  
SANJIVA K. LELE
Keyword(s):  
Mach Number ◽  
High Speed ◽  
Mixture Fraction ◽  
Mixing Layer ◽  
Passive Scalar ◽  
Momentum Equation ◽  
Mixing Efficiency ◽  
Scalar Flux ◽  
Mach Numbers ◽  
Highly Correlated

The mixing of fuel and oxidizer in a mixing layer between high-speed streams is important in many applications, especially air-breathing propulsion systems. The details of this process in a turbulent annular mixing layer are studied with direct numerical simulation. Convective Mach numbers of the simulations range from Mc = 0.1 to Mc = 1.8. Visualizations of the scalar field show that at low Mach numbers large intrusions of nearly pure ambient or core fluid span the mixing region, whereas at higher Mach numbers these intrusions are suppressed. Increasing the Mach number is found to change the mixture fraction probability density function from non-marching to marching and the mixing efficiency from 0.5 at Mc = 0.1 to 0.67 at Mc = 1.5. Scalar concentration fluctuations and the axial velocity fluctuations become highly correlated as the Mach number increases and a suppressed role of pressure in the axial momentum equation is found to be responsible for this. Anisotropy of scalar flux increases with Mc, and is explained via the suppression of transverse turbulence lengthscale.

A Finite-Mode PDF Model for Turbulent Reacting Flows

2001 ◽  
Vol 124 (1) ◽  
pp. 102-107 ◽  
Author(s):  
Kuochen Tsai ◽  
Paul A. Gillis ◽  
Subrata Sen ◽  
Rodney O. Fox
Keyword(s):  
Mixing Layer ◽  
Chemical Species ◽  
Reaction Rates ◽  
Reacting Flows ◽  
Transport Equations ◽  
Turbulent Reacting Flows ◽  
Parallel Reaction ◽  
Consecutive Reaction ◽  
The Mean ◽  
Reactive Mixing

The recently proposed multi-environment model, R. O. Fox, 1998, “On the Relationship between Lagrangian Micromixing Models and Computational Fluid Dynamics,” Chem. Eng. Proc., Vol. 37, pp. 521–535. J. Villermaux and J. C. Devillon, 1994, “A Generalized Mixing Model for Initial Contacting of Reactive Fluids,” Chem. Eng. Sci., Vol. 49, p. 5127, provides a new category of modeling techniques that can be employed to resolve the turbulence-chemistry interactions found in reactive flows. By solving the Eulerian transport equations for volume fractions and chemical species simultaneously, the local concentrations of chemical species in each environment can be obtained. Assuming micromixing occurs only in phase space, the well-known IEM (interaction by exchange with the mean) model can be applied to close the micromixing term. This simplification allows the model to use micromixing timescales obtained from more sophisticated models and can be applied to any number of environments. Although the PDF shape doesn’t change under this assumption, the interaction between turbulence and chemistry can be resolved up to the second moments without any ad-hoc assumptions for the mean reaction rates. Furthermore, the PDF shape is found to have minimal effect on mean reaction rates for incompressible turbulent reacting flows. In this formulation, a spurious dissipation term arises in the transport equation of the scalar variances due to the use of Eulerian transport equations. A procedure is proposed to eliminate this spurious term. The model is applied to simulate the experiment of S. Komori, et al., 1993, “Measurements of Mass Flux in a Turbulent Liquid Flow With a Chemical Reaction,” AIChE J., Vol. 39, pp. 1611–1620, for a reactive mixing layer and the experiment of K. Li and H. Toor, 1986, “Turbulent Reactive Mixing With a Series Parallel reaction: Effect of Mixing on Yield,” AIChE J., Vol. 32, pp. 1312–1320, with a two-step parallel/consecutive reaction. The results are found to be in good agreement with the experimental data of Komori et al. and the PDF simulation of K. Tsai and R. Fox, 1994, “PDF Simulation of a Turbulent Series-Parallel Reaction in an Axisymmetric Reactor,” Chem. Eng. Sci., Vol. 49, pp. 5141–5158, for the experiment of Li and Toor. The resulting model is implemented in the commercial CFD code, FLUENT,1 and can be applied with any number of species and reactions.

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