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Research Papers: Fundamental Issues and Canonical Flows

Scalar Mixing Study at High-Schmidt Regime in a Turbulent Jet Flow Using Large-Eddy Simulation/Filtered Density Function Approach

[+] Author and Article Information
Juan M. Mejía

Departamento de Procesos y Energía,
Universidad Nacional de Colombia,
Cr. 80 No. 65-223,
Medellín 050034, Colombia
e-mail: jmmejiaca@unal.edu.co

Farid Chejne

Departamento de Procesos y Energía,
Universidad Nacional de Colombia,
Cr. 80 No. 65-223,
Medellín 050034, Colombia
e-mail: fchejne@unal.edu.co

Alejandro Molina

Departamento de Procesos y Energía,
Universidad Nacional de Colombia,
Cr. 80 No. 65-223,
Medellín 050034, Colombia
e-mail: amolinao@unal.edu.co

Amsini Sadiki

Institute of Energy and Power Plant Technology,
Technischen Universität Darmstadt,
Jovanka-Bontschits-Str. 2,
Darmstadt D-64287, Germany
e-mail: sadiki@ekt.tu-darmstadt.de

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 7, 2014; final manuscript received September 15, 2015; published online October 26, 2015. Assoc. Editor: Samuel Paolucci.

J. Fluids Eng 138(2), 021205 (Oct 26, 2015) (9 pages) Paper No: FE-14-1731; doi: 10.1115/1.4031631 History: Received December 07, 2014; Revised September 15, 2015

Mixing of a passive scalar in a high-Schmidt turbulent round jet was studied using large-eddy simulation (LES) coupled to filtered density function (FDF). This coupled approach enabled the solution of the continuity, momentum, and scalar (concentration) transport equations when studying mixing in a confined turbulent liquid jet discharging a conserved scalar (rhodamine B) into a low-velocity water stream. The Monte Carlo method was used for solving the FDF transport equation and controlling the number of particles per cell (NPC) using a clustering and splitting algorithm. A sensibility analysis of the number of stochastic particles per cell as well as the influence of the subgrid-scale (SGS) mixing time constant were evaluated. The comparison of simulation results with experiments showed that LES/FDF satisfactorily reproduced the behavior observed in this flow configuration. At high radial distances, the developed superviscous layer generates an intermittency phenomenon leading to a complex, anisotropic behavior of the scalar field, which is difficult to simulate with the conventional and advanced SGS models required by LES. This work showed a close agreement with reported experimental data at this superviscous layer following the FDF approach.

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References

Figures

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Fig. 1

Instantaneous radial distribution of the FDF at different axial positions (θ = 0) at (a) z/D = 15, (b) z/D = 30, (c) z/D = 50, (d) z/D = 60, (e) z/D = 70, and (f) z/D = 80

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Fig. 2

Simulation of the streamwise concentration distribution along the jet centerline, with NPC as parameter

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Fig. 8

Power spectra density of the scalar field using the SGS mixing time constant as parameter. Continuous line: CΩ = 1; dashed: CΩ = 8; and dotted: CΩ = 12.

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Fig. 3

Predicted mean (a) and RMS (b) concentration distribution across the jet at different downstream locations

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Fig. 4

Radial mean (a) and fluctuation (b) concentration distribution across the jet for different cells in the axial direction. FDF simulation results (lines). Experimental data from Antoine et al. [33] (symbols): + x/D = 70; • x/D = 80; and × x/D = 90.

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Fig. 5

Radial mean (a) and fluctuation (b) concentration distribution across the jet for different cells in radial direction. FDF simulation results (lines). Experimental data from Antoine et al. [33] (symbols): + x/D = 70; • x/D = 80; and × x/D = 90.

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Fig. 6

Radial mean (a) and fluctuation (b) concentration distribution across the jet for different cells in angular direction. FDF simulation results (lines). Experimental data from Antoine et al. [33] (symbols): + x/D = 70; • x/D = 80; and × x/D = 90.

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Fig. 7

SGS streamwise (a) and radial (b) scalar flux distribution across the jet. FDF simulation results (lines). Experimental data from Antoine et al. [33] (symbols): + x/D = 70; • x/D = 80; and × x/D = 90.

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Fig. 10

Streamwise mean concentration distribution. Lines: simulation results. Symbol: experimental data from Antoine et al. [33]. The SGS mixing time constant, CΩ, appears as parameter.

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Fig. 9

Time-averaged skewness (a) and kurtosis (b) of the FDF using the SGS mixing time constant as parameter. Continuous line: CΩ = 1; dashed: CΩ = 8; and dotted: CΩ = 12.

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