Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

High Fidelity Simulation of the Spray Generated by a Realistic Swirling Flow Injector

[+] Author and Article Information
Xiaoyi Li

Staff Research Engineer
United Technologies Research Center,
411 Silver Lane,
East Hartford, CT 06108
e-mail: lixy2@utrc.utc.com

Marios C. Soteriou

United Technologies Research Center,
411 Silver Lane,
East Hartford, CT 06108
e-mail: soterimc@utrc.utc.com

Wookyung Kim

Senior Research Engineer
United Technologies Research Center,
411 Silver Lane,
East Hartford, CT 06108
e-mail: kimw@utrc.utc.com

Jeffrey M. Cohen

United Technologies Research Center,
411 Silver Lane,
East Hartford, CT 06108
e-mail: cohenjm@utrc.utc.com

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 8, 2014; final manuscript received January 13, 2014; published online February 18, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(7), 071503 (Feb 18, 2014) (10 pages) Paper No: GTP-14-1005; doi: 10.1115/1.4026531 History: Received January 08, 2014; Revised January 13, 2014

Practical aero-engine fuel injection systems are highly complicated, combining complex fuel atomizer and air swirling elements to achieve good fuel-air mixing and long residence time in order to enhance both the combustion efficiency and stability. While a detailed understanding of the multiphase flow processes occurring in a realistic injector has been limited due to the complex geometries and the challenges in near-field measurements, high fidelity, first principles simulation offers, for the first time, the potential for a comprehensive physics-based understanding. In this work, such simulations have been performed to investigate the spray atomization and subsequent droplet transport in a swirling air stream generated by a complex multinozzle/swirler combination. A coupled level set and volume of fluid (CLSVOF) approach is used to directly capture the liquid-gas interface and an embedded boundary (EB) method is applied to flexibly handle the complex injector geometry. The ghost fluid (GF) method is also used to facilitate simulations at a realistic fuel-air density ratio. Adaptive mesh refinement (AMR) and Lagrangian droplet models are used to efficiently resolve the multiscale processes. To alleviate the global constraint on the time step imposed by the locally activated AMR near liquid jets, a separate AMR simulation focusing on jet atomization was performed for a relatively short physical time and the resulting Lagrangian droplets are coupled into another simulation on a uniform grid at larger time-steps. The high cost simulations were performed at the U.S. Department of Defense high performance computing facilities using over 5000 processors. Experiments at the same flow conditions were conducted at the United Technologies Research Center (UTRC). The simulation details of flow velocity and vorticity due to the interaction of the fuel jet and swirling air are presented. The velocity magnitude is compared with the experimental measurement at two downstream planes. The two-phase spray spreading is compared with experimental images and the flow details are further analyzed to enhance the understanding of the complex physics.

Copyright © 2014 by ASME
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Fig. 1

Sketch of a high-shear nozzle/swirler combination

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Fig. 2

(a) Schematics for the embedded boundary approach definition of an additional level set function for the solid surface. (b) A typical cell partially cut by the solid surface.

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Fig. 3

Photographs of the ambient spray rig at UTRC: (a) large scale view, (b) zoomed-in view of the swirler setup, and (c) zoomed-in view of the fuel nozzle details

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Fig. 4

Sketch of the measurement techniques used at the UTRC ambient spray facility: (a) patternation, and (b) phase Doppler interferometry (PDI)

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Fig. 5

Sketch of the simulation domain and boundary conditions

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Fig. 6

Uniform-grid scalability of the simulation code on high performance computers. The red curves describe the weak scalability of the nonoptimized code. The blue curves describe the weak scalability of the optimized code. The strong scalability at particular problem sizes is described by the black curves and the current problem is indicated by the bold black curve.

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Fig. 7

Sketch for the two-stage coupled simulation approach: (a) reduced-domain AMR simulation, and (b) full-domain uniform grid simulation

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Fig. 8

Close-up view of the flow details within the swirler in the full domain uniform grid simulation. Instantaneous snapshots of the (a) velocity magnitude, and (b) vorticity magnitude at a plane cut through the domain center. The three-dimensional liquid surfaces resolved by the coarse grid are also displayed.

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Fig. 9

Velocity and vorticity magnitude at a plane cut through the domain center for the full domain simulation: (a) and (c) instantaneous snapshots, and (b) and (d) average over 5 μs in physical time

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Fig. 10

Comparison of the simulated air velocity at two downstream planes (left, 1.1 in.; right, 1.6 in.) with experimental measurements: (a) experiments, (b) simulation (instantaneous), and (c) simulation (average over 5 ms)

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Fig. 11

(a) Grid-refinement near fuel nozzles in the reduced-domain simulation. (b) Detailed liquid surfaces resolved by the refined grid. The vorticity magnitude color contour is shown on the liquid surface.

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Fig. 12

Comparison of the uniform grid simulation (left) with the AMR simulation (right): (a) and (b) instantaneous liquid structures and vorticity field at the center plane, and (c) and (d) instantaneous liquid jet breakup near fuel nozzles viewed from the exit of the injector

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Fig. 13

Comparison of the near-field liquid jet's details: (a) experimental high-speed image, and (b) simulation

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Fig. 14

(a) Snapshot of the velocity magnitude, and (b) droplet spreading for the coupled simulation at t = 21 μs, and (c) experimental high-speed image of droplet spreading



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