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

8. 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

10. 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

11. 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

16. 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

24. 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

27. 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

29. 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

30. 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

31. 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

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

33. RANS for Forced Mixers

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

35. What is a Lobe Mixer?

36. 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.

37. 3-D Mesh

38. WIND Code options 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

39. Grid Dependence Density Contours 1.7 million grid points

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

41. Spalart-Allmaras and Menter SST Turbulence Models

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

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

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

45. 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.

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

47. 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.

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

49. 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

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

51. 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

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

53. Four-Source Coaxial Jet Noise PredictionVs Vp
Initial Region
Interaction Region
Mixed Flow Region
Secondary / Ambient Shear Layer
Primary / Secondary Shear Layer

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

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

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

57. 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

58. Noise Prediction Comparisons
Low Penetration Mixer
High Penetration Mixer

59. Noise Prediction Comparisons
Low Penetration Mixer
High Penetration Mixer

60. Noise Prediction Comparisons
Low Penetration Mixer
High Penetration Mixer

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

62. Modified Formulation Variable Parameters
DdB
Dfc

63. 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
DdBm ,DdBe fs
Mid Frequency Sub-Domain
DdBs ,DdBm ,DdBe
fs , fm , fe
High Frequency Sub-Domain
DdBs
fm ,fe

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

65. Parameter Optimization Results
Low Penetration Mixer
High Penetration Mixer

66. Modified Method with Optimized Parameters
Low Penetration Mixer
High Penetration Mixer

67. Modified Method with Optimized Parameters
Low Penetration Mixer
High Penetration Mixer

68. Modified Method with Optimized Parameters
Low Penetration Mixer
High Penetration Mixer

69. Optimized Parameter Trends
DdBs (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

70. Optimized Parameter Trends
DdBm (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

71. Optimized Parameter Trends
fc (Increased)
Influenced by the location where the turbulent mixing layer generated by the lobe mixer intersects the ambient-exhaust shear layer

72. 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

73. Conclusion
Methodologies (LES, RANS, semi-empirical method) are being developed to study noise from forced mixers