1 / 82

Development of Low Jet Noise Aircraft Engines

Development of Low Jet Noise Aircraft Engines. Anastasios Lyrintzis School of Aeronautics & Astronautics Purdue University. Acknowledgements. Indiana 21 st Century Research and Technology Fund Prof. Gregory Blaisdell Rolls-Royce, Indianapolis (W. Dalton, Shaym Neerarambam)

sullins
Télécharger la présentation

Development of Low Jet Noise Aircraft Engines

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 • (Experiments at NASA Glenn)

  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 • Cut-off Strouhal number based on grid resolution is around 1.0

  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 • Evaluate noise sources (Tij) • 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: • Noise from unresolved scales • Supersonic flow • Solid boundaries (nozzle 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. Nozzle Bypass Flow Mixer Exhaust Flow Core Flow Tail Cone Lobed Mixer Mixing Layer Exhaust / Ambient Mixing Layer Internally Forced Mixed Jet

  21. Forced Mixer H H: Lobe Penetration (Lobe Height)

  22. 3-D Mesh

  23. 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

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

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

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

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

  28. Mean Axial Velocity at x = 2.88”(High Penetration) Spalart Allmaras ¼ Scale Spalart at x = 2.88/4” experiment

  29. Mean Axial Velocity at x = 2.88”(High Penetration) ¼ Scale Menter SST at x = 2.88/4” experiment Menter SST

  30. 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.

  31. Geometry at Mixer Exit Low Penetration Mid Penetration High Penetration

More Related