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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 & Astronautics Purdue University, West Lafayette, IN. January 6, 2004 42 nd Aerospace Sciences Meeting and Exhibit Reno, NV AIAA 2004-0516. Motivation.
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Effects of Inflow Forcing on Jet Noise Using Large Eddy SimulationP. Lew, A. Uzun, G. A. Blaisdell & A. S. LyrintzisSchool of Aeronautics & AstronauticsPurdue University, West Lafayette, IN.January 6, 200442nd Aerospace Sciences Meeting and ExhibitReno, NVAIAA 2004-0516
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
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
Objective • Using our LES methodology, investigate and establish further trends on the effects of inflow forcing on: • Turbulent flow development • Far-field jet noise
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
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
Vortex Ring Forcing • Proposed by Bogey et al. 2003 • Simplified expressions • Total number of modes = nmodes + 1
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)
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
Vortex Ring Forcing Setup (Cont’d)
Results – Growth rates • Under-prediction due to short domain length • Need x > 45ro to get correct growth rates
Potential Core Potential Core Lengths • 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
Turbulence intensities – radial (within shear layer, r = ro)
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
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
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
Recommendations • Extend computational domain to about 60ro • Computationally costly • Include part of nozzle geometry for LES to alleviate uncertainty of forcing
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