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Research Papers

Large Eddy Simulation of Scalar Mixing in Jet in a Cross-Flow

[+] Author and Article Information
Asela Uyanwaththa

School of Mechanical, Electrical and
Manufacturing Engineering,
Loughborough University,
Loughborough LE11 3TU, UK
e-mail: a.r.uyanwaththa@lboro.ac.uk

Weeratunge. Malalasekera

School of Mechanical, Electrical and
Manufacturing Engineering,
Loughborough University,
Loughborough LE11 3TU, UK
e-mail: w.malalasekera@lboro.ac.uk

Graham Hargrave

School of Mechanical, Electrical and
Manufacturing Engineering,
Loughborough University,
Loughborough LE11 3TU, UK
e-mail: G.K.Hargrave@lboro.ac.uk

Mark Dubal

Uniper Technologies Limited,
Technology Centre Ratcliffe-on-Soar,
Nottinghamshire NG11 0EG, UK
e-mail: Mark.Dubal@uniper.energy

1Corresponding author.

Manuscript received June 20, 2018; final manuscript received November 21, 2018; published online January 9, 2019. Assoc. Editor: Eric Petersen.

J. Eng. Gas Turbines Power 141(6), 061005 (Jan 09, 2019) (13 pages) Paper No: GTP-18-1266; doi: 10.1115/1.4042089 History: Received June 20, 2018; Revised November 21, 2018

Jet in a cross-flow (JICF) is a flow arrangement found in many engineering applications, especially in gas turbine air–fuel mixing. Understanding of scalar mixing in JICF is important for low NOx burner design and operation, and numerical simulation techniques can be used to understand both spatial and temporal variation of air–fuel mixing quality in such applications. In this paper, mixing of the jet stream with the cross-flow is simulated by approximating the jet flow as a passive scalar and using the large eddy simulation (LES) technique to simulate the turbulent velocity field. A posteriori test is conducted to assess three dynamic subgrid scale models in modeling jet and cross-flow interaction with the boundary layer flow field. Simulated mean and Reynolds stress component values for velocity field and concentration fields are compared against experimental data to assess the capability of the LES technique, which showed good agreement between numerical and experimental results. Similarly, time mean and standard deviation values of passive scalar concentration also showed good agreement with experimental data. In addition, LES results are further used to discuss the scalar mixing field in the downstream mixing region.

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Figures

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Fig. 1

Computational domain

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Fig. 10

Mean velocity field and Reynolds stress field at z =1.5D: left: experimental data [16]; right: DSM

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Fig. 5

Mean velocity and Reynolds stress component at different on y =0 plane, across z =1D (experimental data: [16])

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Fig. 8

Normalized mean Reynolds stress components variation on the symmetric plane y =0D (experimental data: [18])

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Fig. 9

Mean velocity and Reynolds stress variation in downstream direction (experimental data: [16])

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Fig. 11

Passive scalar concentration variation in z direction with increasing downstream distance (experimental data: [17])

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Fig. 12

Passive scalar concentration variation in x direction with increasing jet direction distance (experimental data: [16])

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Fig. 2

Upstream instantaneous velocity field

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Fig. 3

Mesh sensitivity of three SGS models compared at x =0.5D (experimental data: [16])

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Fig. 4

Resolved turbulent kinetic energy percentage and Reynolds stress: (a) resolved turbulent kinetic energy percentage (γ) and (b) total Reynolds stress and resolved Reynolds stress

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Fig. 6

Normalized LES mean velocity components variation on the symmetric plane (experimental data: [18])

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Fig. 7

Mean velocity field distribution and mean stream lines on z =1.5D plane

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Fig. 13

Mean passive scalar contours on z =1.5D plane (experimental data: [16])

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Fig. 16

Passive scalar field distribution: (a) passive scalar concentration on the symmetry plane (y = 0D) and (b) mean and standard deviation of passive scalar concentration on the plane x = 3D

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Fig. 17

Passive scalar gradient in cross-flow direction

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Fig. 14

Vortex structures of JICF

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Fig. 15

Vortex structures realized using λ2 criteria

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