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Development of Low Jet Noise Aircraft Engines

This study focuses on the development and validation of a computational aeroacoustics methodology for predicting jet noise in aircraft engines. The research includes 3-D Large Eddy Simulation for jet aeroacoustics, coupling between LES and RANS solutions, and a semi-empirical method for mixer noise. The results show a good comparison with experiments and provide valuable evidence towards the full validation of the methodology.

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Development of Low Jet Noise Aircraft Engines

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  1. Development of Low Jet Noise Aircraft Engines Anastasios Lyrintzis School of Aeronautics & Astronautics Purdue University

  2. Acknowledgements • Indiana 21st Century Research and Technology Fund • Prof. Gregory Blaisdell • Rolls-Royce, Indianapolis (W. Dalton, Shaym Neerarambam) • L. Garrison, C. Wright, A. Uzun, P-T. Lew

  3. Motivation • Airport noise regulations are becoming stricter. • Jet exhaust noise is a major component of aircraft engine noise • Lobe mixer geometry has an effect on the jet noise that needs to be investigated.

  4. Methodology • 3-D Large Eddy Simulation for Jet Aeroacoustics • RANS for Forced Mixers • Coupling between LES and RANS solutions • Semi-empirical method for mixer noise

  5. 3-D Large Eddy Simulation for Jet Aeroacoustics

  6. Objective • Development and full validation of a Computational Aeroacoustics (CAA) methodology for jet noise prediction using: • A 3-D Large Eddy Simulation (LES) code working on generalized curvilinear grids that provides time-accurate unsteady flow field data • A surface integral acoustics method using LES data for far-field noise computations

  7. Numerical Methods for LES • 3-D Navier-Stokes equations • 6th-order accurate compact differencing scheme for spatial derivatives • 6th-order spatial filtering for eliminating instabilities from unresolved scales and mesh non-uniformities • 4th-order Runge-Kutta time integration • Localized dynamic Smagorinsky subgrid-scale (SGS) model for unresolved scales

  8. Computational Jet Noise Research • Some of the biggest jet noise computations: • Freund’s DNS for ReD = 3600, Mach 0.9 cold jet using 25.6 million grid points (1999) • Bogey and Bailly’s LES for ReD = 400,000, Mach 0.9 isothermal jets using 12.5 and 16.6 million grid points (2002, 2003) • We studied a Mach 0.9 turbulent isothermal round jet at a Reynolds number of 100,000 • 12 million grid points used in our LES

  9. Computation Details • Physical domain length of 60ro in streamwise direction • Domain width and height are 40ro • 470x160x160 (12 million) grid points • Coarsest grid resolution: 170 times the local Kolmogorov length scale • One month of run time on an IBM-SP using 160 processors to run 170,000 time steps • Can do the same simulation on the Compaq Alphaserver Cluster at Pittsburgh Supercomputing Center in 10 days

  10. Mean Flow Results • Our mean flow results are compared with: • Experiments of Zaman for initially compressible jets (1998) • Experiment of Hussein et al. (1994) Incompressible round jet at ReD = 95,500 • Experiment of Panchapakesan et al. (1993) Incompressible round jet at ReD = 11,000

  11. Jet Aeroacoustics • Noise sources located at the end of potential core • Far field noise is estimated by coupling near field LES data with the Ffowcs Williams–Hawkings (FWH) method • Overall sound pressure level values are computed along an arc located at 60ro from the jet nozzle

  12. Jet Aeroacoustics (continued) • OASPL results are compared with: • Experiment of Mollo-Christensen et al. (1964) Mach 0.9 round jet at ReD = 540,000 (cold jet) • Experiment of Lush (1971) Mach 0.88 round jet at ReD = 500,000 (cold jet) • Experiment of Stromberg et al. (1980) Mach 0.9 round jet at ReD =3,600 (cold jet) • SAE ARP 876C database

  13. Conclusions • Localized dynamic SGS model stable and robust for the jet flows we are studying • Very good comparison of mean flow results with experiments • Aeroacoustics results are encouraging • Valuable evidence towards the full validation of our CAA methodology has been obtained

  14. Near Future Work • Simulate Bogey and Bailly’s ReD = 400,000 jet test case using 16 million grid points • 100,000 time steps to run • About 150 hours of run time on the Pittsburgh cluster using 200 processors • Compare results with those of Bogey and Bailly to fully validate CAA methodology • Do a more detailed study of surface integral acoustics methods

  15. Can a realistic LES be done for ReD = 1,000,000 ? • Assuming 50 million grid points provide sufficient resolution: • 200,000 time steps to run • 30 days of computing time on the Pittsburgh cluster using 256 processors • Only 3 days on a near-future computer that is 10 times faster than the Pittsburgh cluster

  16. Future Work • Extend methodology to handle • Supersonic flow • Solid boundaries (lips) • Complicated (mixer) geometries multi-block code

  17. RANS for Forced Mixers

  18. Objective • Use RANS to study flow characteristics of various flow shapes

  19. What is a Lobe Mixer?

  20. Current Progress • Only been able to obtain a ‘high penetration’ mixer for CFD analysis. • Have completed all of the code and turbulence model comparisons with single mixer.

  21. 3-D Mesh

  22. 2nd order upwind scheme 1.7 million/7 million grid points 8-16 zones 8-16 LINUX processors Spalart-Allmaras/ SST turbulence model Wall functions WIND Code options

  23. Grid Dependence Density Contours 1.7 million grid points Density Contours 7 million grid points

  24. Grid Dependence 1.7 million grid points 7 million grid points Density Vorticity Magnitude

  25. Spalart-Allmaras and Menter SST Turbulence Models Spalart-Allmaras Menter SST

  26. Spalart-Allmaras and and Menter SST at Nozzle Exit Plane SST Spalart Density Vorticity Magnitude

  27. Turbulence Intensity at x/d = .4 Menter SST model Experiment, NASA Glenn 1996 WIND

  28. Mean Axial Velocity at x/d = .4 Spalart-Allmaras Menter SST Experiment, NASA Glenn 1996 WIND WIND

  29. Spalart-Allmaras vs. Menter SST • The Spalart-Allmaras model appears to be less dissipative. The vortex structure is sharper and the vorticity magnitude is higher at the nozzle exit. • The Menter SST model appears to match experiments better, but the experimental grid is rather coarse and some of the finer flow structure may have been effectively filtered out. • Still unclear which model is superior. No need to make a firm decision until several additional geometries are obtained.

  30. Preliminary Conclusions • 1.7 million grid is adequate • Further work is needed comparing the turbulence models

  31. Future Work • Analyze the flow fields and compare to experimental acoustic and flow-field data for additional mixer geometries. • Further compare the two turbulence models. • If possible, develop qualitative relationship between mean flow characteristics and acoustic performance.

  32. Implementing RANS Inflow Boundary Conditions for 3-D LES Jet Aeroacoustics

  33. Objectives • Implement RANS solution and onto 3-D LES inflow BCs as initial conditions. • Investigate the effect of RANS inflow conditions on: • Reynolds Stresses • Far-field sound generated

  34. Implementation Method LES RANS • RANS grid too fine for LES grid to match. • Since RANS grid has high resolution, linear interpolation will be used.

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