1 effects of inflow forcing on jet noise using large eddy simulation p. lew, a. uzun, g. a....

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1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics Purdue University, West Lafayette, IN. January 6, 2004 42 nd Aerospace Sciences Meeting and Exhibit Reno, NV AIAA 2004-0516

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Page 1: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

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Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation

P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis

School of Aeronautics & AstronauticsPurdue University, West Lafayette, IN.

January 6, 200442nd Aerospace Sciences Meeting and Exhibit

Reno, NV

AIAA 2004-0516

Page 2: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

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Motivation• Current CFD calculations which include the jet

nozzle are mostly restricted to a Reynolds Average Navier-Stokes (RANS) approach– Prohibitive number of grid points to resolve the

shear layer in LES.

• Forcing yields a way to replace a jet nozzle for LES– Pros: Computationally cheap and easy to implement– Cons: Results are sensitive to forcing parameters

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Motivation (cont’d)

• Flow development and far-field noise of the jet were affected when selected parameters were changed in the inflow forcing (Bogey & Bailly 2003, Bodony & Lele 2003)

• Parameter that had the greatest impact was the number of azimuthal forcing modes– Used 16 modes in total– Removing the first 4 modes resulted in a more

quiet jet

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Objective

• Using our LES methodology, investigate and establish further trends on the effects of inflow forcing on:

– Turbulent flow development

– Far-field jet noise

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LES Methodology• LES methodology developed by Uzun

et al. (AIAA 2003-3322)

– 6th-order compact scheme for interior nodes

– 4th-order centered compact scheme for points next to the boundaries

– 3rd-order one-sided compact scheme for boundary nodes

– Sponge zone is attached downstream of the physical domain

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LES Methodology (Cont’d)

– Tam & Dong’s 3-D radiation and outflow BCs on boundaries

– 6th-order tri-diagonal compact spatial filter used as an implicit SGS model Smagorinsky results sensitive to Csgs

Localized dynamic SGS model computationally expensive (50% increase in CPU)

Only looking for trends

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LES Methodology (Cont’d)

Tam & Dong' s radiation boundary conditions

Tam & Dong' s radiation boundary conditions

Tam & Dong' soutflow boundaryconditions

Sponge zone

Tam &Dong' sradiationbcs

Vortex ring forcing

Page 8: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

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Vortex Ring Forcing

• Proposed by Bogey et al. 2003• Simplified expressions

• Total number of modes = nmodes + 1

modes

modes

0 ,0

'

0 ,0

'

cos( )

cos( )

x

r

n

x x x ring o n nn

v

n

r r r ring o n nn

v

v v U U n

v v U U n

Page 9: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

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Setup

• Domain size:

(x, y, z) = (25, ±15, ±15) ro

• Grid points:

(Nx, Ny, Nz) = 287 x 128 x 128

• Approx. 4.7 x 106 points (Every other grid point is shown)

Page 10: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

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Setup (Cont’d)• Jet inflow conditions

– Mach 0.9 and Re = 100,000 (Isothermal Jet)

• Runtime for one case: 4 days on 64 CPUs (IBM SP3)

• Original setup has 16 modes in total

Test case name

No. of modes removed

Baseline None (16)

rf4 First Four Modes (12)

rf6 First Six Modes (10)

rf8 First Eight Modes (08)

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Setup (Cont’d)

Vortex Ring Forcing

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Results – Growth rates

• Under-prediction due to short domain length

• Need x > 45ro to get correct growth rates

Test Case

Growth

Baseline 0.076

rf4 0.071

rf6 0.074

rf8 0.078

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Potential Core Lengths

Test Case Location

Baseline 11.54ro

rf4 13.07ro

rf6 13.43ro

rf8 13.45ro Potential Core

• Jet develops slower as more modes are removed– Experiments: Transitional jet = 10ro, (Raman,

1994) Initially turbulent jet = 14ro (Arakeri, 2002)

• Current observation is in good agreement with Bogey and Bailly’s numerical experiments

Page 14: 1 Effects of Inflow Forcing on Jet Noise Using Large Eddy Simulation P. Lew, A. Uzun, G. A. Blaisdell & A. S. Lyrintzis School of Aeronautics & Astronautics

14Turbulence intensities – axial (within shear layer, r = ro)

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15Turbulence intensities – radial (within shear layer, r = ro)

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Turbulence intensities

• Shift in peak turbulence intensities due to a longer potential core– Trends so far agree well with Bogey

• However, radial peak intensities show unexpected increases for rf6 & rf8– Uzun (2003) reported a similar observation for

rf6 (M=0.9, Re = 400,000)– Further investigation is needed

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Far Field Aeroacoustics• Methodology

– Ffowcs Williams-Hawkings surface integral acoustic technique: Open and closed control surfaces are used

– Acoustic data collected every five time steps over 25,000 time steps

– Based on current spatial grid resolution we resolve a maximum Strouhal number of St = 1.1

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Far Field Aeroacoustics

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OASPL at 60ro (Open Control Surface)

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20OASPL at 60ro (Closed Control Surface)

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SPL at r = 60ro @ = 60o (Open CS)

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SPL at r = 60ro @ = 60o (Closed CS)

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Conclusions

• The effect of removing modes for a vortex ring forcing was studied for an LES code (with a filter used as an SGS model)

• As more modes are removed – the potential core becomes longer– the peak radial turbulence intensities

increase– OASPL increases slightly

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Recommendations

• Extend computational domain to about 60ro

– Computationally costly

• Include part of nozzle geometry for LES to alleviate uncertainty of forcing

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Acknowledgements

• Indiana 21st Research Century & Technology Fund

• National Computational Science Alliance under grant CT0100032N

• Simulations were run on SGI Origin 2000 and IBM SP4 at UIUC, Urbana-Champaign

• Also utilized Purdue University’s 320-node and Indiana University’s 600 node IBM SP3 supercomputers