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

2. Acknowledgements Indiana 21 st 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. 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 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 6 th -order accurate compact differencing scheme for spatial derivatives 6 th -order spatial filtering for eliminating instabilities from unresolved scales and mesh non-uniformities 4 th -order Runge-Kutta time integration Localized dynamic Smagorinsky subgrid-scale (SGS) model for unresolved scales

9. Computational Jet Noise Research Some of the biggest jet noise computations:  Freund’s DNS for Re D = 3600, Mach 0.9 cold jet using 25.6 million grid points (1999)  Bogey and Bailly’s LES for Re D = 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

10. Computation Details Physical domain length of 60r o in streamwise direction Domain width and height are 40r o 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

15. 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 Re D = 95,500  Experiment of Panchapakesan et al. (1993) Incompressible round jet at Re D = 11,000

25. 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 60r o from the jet nozzle Both near and far field acoustic pressure spectra are computed Assuming at least 6 grid points are required per wavelength, cut-off Strouhal number is around 1.0

28. OASPL results are compared with:  Experiment of Mollo-Christensen et al. (1964) Mach 0.9 round jet at Re D = 540,000 (cold jet)  Experiment of Lush (1971) Mach 0.88 round jet at Re D = 500,000 (cold jet)  Experiment of Stromberg et al. (1980) Mach 0.9 round jet at Re D =3,600 (cold jet)  SAE ARP 876C database Acoustic pressure spectra are compared with Bogey and Bailly’s Re D = 400,000 isothermal jet Jet Aeroacoustics (continued)

31. Conclusions Localized dynamic SGS model very 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

32. Near Future Work Simulate Bogey and Bailly’s Re D = 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

33. Can a realistic LES be done for Re D = 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

34. RANS for Forced Mixers

35. Objective Use RANS to study flow characteristics of various flow shapes

36. What is a Lobe Mixer?

37. Lobe Penetration

38. 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.

39. 3-D Mesh

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

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

42. Grid Dependence 1.7 million grid points7 million grid points Density Vorticity Magnitude

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

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

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

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

47. Turbulence Intensity at x/d = 1.0 Menter SST model Experiment, NASA Glenn 1996 WIND

48. Mean Axial Velocity at x/d = 1.0 Experiment, NASA Glenn 1996 Spalart-AllmarasMenter SST WIND

49. 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.

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

51. 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.

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

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

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

55. Issues and Challenges Accurate resolution of outgoing vortex with LES grid. Accurate resolution of shear layer near nozzle lip. May need to use an intermediate Reynolds number eg. Re = 400,000

56. An Investigation of Extensions of the Four- Source Method for Predicting the Noise From Jets With Internal Forced Mixers

57. Four-Source Coaxial Jet Noise Prediction VsVs VsVs VpVp Initial Region Interaction Region Mixed Flow Region Secondary / Ambient Shear Layer Primary / Secondary Shear Layer

58. –Secondary Jet: –Effective Jet: –Mixed Jet: –Total noise is the incoherent sum of the noise from the three jets Four-Source Coaxial Jet Noise Prediction

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

60. Internally Forced Mixed Jet Bypass Flow Mixer Core Flow Nozzle Tail Cone Exhaust Flow Exhaust / Ambient Mixing Layer Lobed Mixer Mixing Layer

61. Noise Prediction Comparisons Experimental Data –Aeroacoustic Propulsion Laboratory at NASA Glenn –Far-field acoustic measurements (~80 diameters) Single Jet Prediction –Based on nozzle exhaust properties (V,T,D) –SAE ARP876C Coaxial Jet Prediction –Four-source method –SAE ARP876C for single jet predictions

62. Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

63. Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

64. Noise Prediction Comparisons Low Penetration MixerHigh Penetration Mixer

65. Modified Four-Source Formulation Variable Parameters: Single Jet Prediction Source Reduction Spectral Filter

66. Modified Formulation Variable Parameters  dB fcfc fcfc

67. Parameter Optimization Algorithm Frequency range is divided into three sub-domains Start with uncorrected single jet sources Evaluate the error in each frequency sub-domain and adjusted relevant parameters Iterate until a solution is converged upon Low Frequency Sub-Domain  dB m,  dB e f s Mid Frequency Sub-Domain  dB s,  dB m,  dB e f s, f m, f e High Frequency Sub-Domain  dB s f m,f e

68. Parameter Optimization Algorithm Mid Frequency Sub-Domain High Frequency Sub-Domain Low Frequency Sub-Domain

69. Parameter Optimization Results Low Penetration Mixer High Penetration Mixer

70. Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

71. Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

72. Modified Method with Optimized Parameters Low Penetration MixerHigh Penetration Mixer

73. Optimized Parameter Trends  dB s (Increased) –Influenced by the convergent nozzle and mixing of the secondary flow with the faster primary flow –The exhaust jet velocity will be greater than the secondary jet velocity resulting in a noise increase

74. Optimized Parameter Trends  dB m (Decreased) –Influenced by the effect of the interactions of the mixing layer generated by the mixer with the outer ambient- exhaust shear layer –The mixer effects cause the fully mixed jet to diffuse faster resulting in a larger effective diameter and therefore a lower velocity, resulting in a noise reduction

75. Optimized Parameter Trends f c (Increased) –Influenced by the location where the turbulent mixing layer generated by the lobe mixer intersects the ambient- exhaust shear layer

76. Summary In general the coaxial and single jet prediction methods do not accurately model the noise from jets with internal forced mixers The forced mixer noise spectrum can be matched using the combination of two single jet noise sources Currently not a predictive method Next step is to evaluate the optimized parameters for additional mixer data –Additional Mixer Geometries –Additional Flow Conditions (Velocities and Temperatures) Identify trends and if possible empirical relationships between the mixer geometries and their optimized parameters

77. Conclusion Methodologies (LES, RANS, semi- empirical method) have been developed to study noise from forced mixers