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Development of Low-Noise Aircraft Engines
Anastasios Lyrintzis
School of Aeronautics & Astronautics
Purdue University
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
Motivation
• Airport noise regulations are becoming stricter.
• Lobe mixer geometry has an effect on the jet noise that needs to be investigated.
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
3-D Large Eddy Simulation for Jet Aeroacoustics
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
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
Tam & Dong' s radiation boundary conditions
Tam & Dong' s radiation boundary conditions
Tam & Dong' soutflow boundaryconditions
Sponge zone
Tam &Dong' sradiationbcs
Vortex ring forcing
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
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
x / ro
y/r
o
0 10 20 30 40 50 60 70-20
-10
0
10
20
30
40
z / ro
y/r
0
-20 -10 0 10 20-20
-15
-10
-5
0
5
10
15
20
x = 5ro
z / ro
y/r
0
-20 -10 0 10 20-20
-15
-10
-5
0
5
10
15
20
x = 15ro
z / ro
y/r
0
-20 -10 0 10 20-20
-15
-10
-5
0
5
10
15
20
x = 35ro
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
x / Dj
Uj/U
c
0 10 20 300
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
slope = 0.161
From Zaman' sexperiments (1998):slope 0.155 for Mj = 0.9
x / Dj
Q/Q
e
10 15 20 25 304
5
6
7
8
9
10
11
slope = 0.267
From Zaman' sexperiments (1998):slope 0.26 for Mj = 0.9
slope = A = 0.092
experimental valuesof A : 0.086 - 0.096
x / ro
r 1/2
/ro
0 5 10 15 20 25 30 35 40 45 50 55 600
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
r / r1/2
u/U
c
0 0.5 1 1.5 2 2.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x = 45ro
x = 50ro
x = 55ro
exp. data of Hussein et. al.exp. data of Panchapakesan et. al.
r / r1/2
xx
0 0.5 1 1.5 2 2.50
0.025
0.05
0.075
0.1
x = 45ro
x = 50ro
x = 55ro
exp. data of Hussein et. al.exp. data of Panchapakesan et. al.
r / r1/2
rr
0 0.5 1 1.5 2 2.50
0.01
0.02
0.03
0.04
0.05
0.06
x = 45ro
x = 50ro
x = 55ro
exp. data of Hussein et. al.exp. data of Panchapakesan et. al.
r / r1/2
0 0.5 1 1.5 2 2.50
0.01
0.02
0.03
0.04
0.05
0.06
x = 45ro
x = 50ro
x = 55ro
exp. data of Hussein et. al.exp. data of Panchapakesan et. al.
r / r1/2
rx
0 0.5 1 1.5 2 2.50
0.005
0.01
0.015
0.02
0.025
x = 45ro
x = 50ro
x = 55ro
exp. data of Hussein et. al.exp. data of Panchapakesan et. al.
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
• 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
X
Y
Z
Control Surface
Control Surface
Jet Flow
x / ro
y/r
o
0 10 20-5
0
5
10
15
R
• 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• Acoustic pressure spectra are compared with
Bogey and Bailly’s ReD = 400,000 isothermal jet
Jet Aeroacoustics (continued)
(deg)
OA
SPL
(dB
)
10 20 30 40 50 60 70 80 90 100 110 120100
102
104
106
108
110
112
114
116
118
120
LES + FWH (isothermal jet)SAE ARP 876C predictionexp. of Mollo-Christensen et al. (cold jet)exp. of Lush (cold jet)exp. of Stromberg et al. (cold jet)
St
SPL
(dB
/St)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 170
80
90
100
110
120
130
Bogey' s spectra at x = 11ro and r = 15ro
Our spectra at x = 11ro and r = 15ro
4th order polynomial fitOur spectra at R = 60ro and = 80o
4th order polynomial fit
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
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
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
RANS for Forced Mixers
Objective
• Use RANS to study flow characteristics of various flow shapes
What is a Lobe Mixer?
Lobe Penetration
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.
3-D Mesh
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
Grid Dependence
Density Contours1.7 million grid points
Density Contours7 million grid points
Grid Dependence
1.7 million grid points 7 million grid points
Density
VorticityMagnitude
Spalart-Allmaras and Menter SST Turbulence Models
Spalart-Allmaras
Menter SST
Spalart-Allmaras and and Menter SST at Nozzle Exit Plane
Spalart SST
Density
VorticityMagnitude
Turbulence Intensity at x/d = .4Menter SST model
Experiment, NASA Glenn 1996
WIND
Mean Axial Velocity at x/d = .4Menter SST
Experiment,NASA Glenn 1996
Spalart-Allmaras
WIND WIND
Turbulence Intensity at x/d = 1.0Menter SST model
Experiment,NASA Glenn 1996
WIND
Mean Axial Velocity at x/d = 1.0
Experiment,NASA Glenn 1996
Spalart-Allmaras Menter SST
WIND WIND
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.
Preliminary Conclusions
• 1.7 million grid is adequate
• Further work is needed comparing the turbulence models
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.
Implementing RANS Inflow Boundary Conditions for 3-D
LES Jet Aeroacoustics
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
Implementation Method
• RANS grid too fine for LES grid to match.
• Since RANS grid has high resolution, linear interpolation will be used.
LES
RANS
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
An Investigation of Extensions of the Four-Source Method for Predicting the Noise From Jets With Internal Forced Mixers
Four-Source Coaxial Jet Noise Prediction
Vs
Vs
Vp
Initial Region
Interaction Region
Mixed Flow Region
Secondary / Ambient Shear Layer
Primary / Secondary Shear Layer
– Secondary Jet:
– Effective Jet:
– Mixed Jet:
– Total noise is the incoherent sum of the noise from the three jets
ffff s ,Flog10θ,,D,VSPLθ,SPL U10sss
pspepe V,T,TΔdBθ,,D,VSPLθ,SPL ff
ffff ,Flog10θ,,D,VSPLθ,SPL 1D10mmm
sss /DVf
mm1 /DVf
Four-Source Coaxial Jet Noise Prediction
Forced Mixer
H
Lobe Penetration (Lobe Height)
H:
Internally Forced Mixed Jet
Bypass Flow
Mixer
Core Flow
Nozzle
Tail Cone
Exhaust Flow
Exhaust / Ambient Mixing Layer
Lobed Mixer Mixing Layer
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
Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
Modified Four-Source Formulation
Variable Parameters:
sU10ssss dB),(F10log),,D,T,SPL(V),(SPL ffff s
mD10mmmm dB),(F10log),,D,T,SPL(V),(SPL ffff m
eD10eppe dB),(F10log),,D,T,SPL(V),(SPL ffff e
Single Jet Prediction
Source Reduction
Spectral Filter
(dB) Reductions Source ΔdB,ΔdB,ΔdB
sFrequencie off-CutFilter Spectral ,,
mes
mes fff
Modified Formulation Variable Parameters
dB
dB
fc fc
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
dBm ,dBe
fs
Mid Frequency Sub-Domain
dBs ,dBm ,dBe
fs , fm , fe
High Frequency Sub-Domain
dBs
fm ,fe
Parameter Optimization AlgorithmMid Frequency
Sub-DomainHigh Frequency
Sub-DomainLow Frequency
Sub-Domain
Parameter Optimization ResultsCase dBs
dBm f cMaximum Error [dB]
Average Error [dB]
Optimized Solution
7.85 -3.52 19020 4.7 1.2
Four-Source Method
0.00 0.00 1000 9.2 5.0
Single Jet - - - 7.3 1.4
Case dBsdBm f c
Maximum Error [dB]
Average Error [dB]
Optimized Solution
9.92 -5.74 4982 3.6 1.2
Four-Source Method
0.00 0.00 1000 13.2 5.6
Single Jet - - - 8.1 2.8
Low Penetration
Mixer
High Penetration
Mixer
Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
Optimized Parameter Trends
• dBs (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
Optimized Parameter Trends
• dBm (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
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
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
Conclusion
• Methodologies (LES, RANS, semi-empirical method) have been developed to study noise from forced mixers
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