9 th aiaa/ceas aeroacoustics conference purdue university school of aeronautics and astronautics 1...
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9th AIAA/CEAS Aeroacoustics Conference
1Purdue University School of Aeronautics and Astronautics
An Investigation of Extensions of the Four-Source Method for Predicting the Noise From Jets With Internal Forced Mixers
Loren GarrisonPurdue University
School of Aeronautics and Astronautics
W.N. DaltonRolls-Royce Corporation
A.S Lyrintzis and G.A. BlaisdellPurdue University
School of Aeronautics and Astronautics
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Outline• Summary of the Four-source coaxial jet noise
prediction method
• Internally forced mixed jet configurations
• Comparisons of mixer experimental data to coaxial and single jet predictions
• Modified four-source formulation
• Modified Method Parameter optimization
• Modified Method Results
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Four-Source Coaxial Jet Noise Prediction
Vs
Vs
Vp
Initial Region
Interaction Region
Mixed Flow Region
Secondary / Ambient Shear Layer
Primary / Secondary Shear Layer
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– 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
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5Purdue University School of Aeronautics and Astronautics
Forced Mixer
H
Lobe Penetration (Lobe Height)
H:
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Internally Forced Mixed Jet
Bypass Flow
Mixer
Core Flow
Nozzle
Tail Cone
Exhaust Flow
Exhaust / Ambient Mixing Layer
Lobed Mixer Mixing Layer
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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
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Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
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Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
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Noise Prediction Comparisons
Low Penetration Mixer High Penetration Mixer
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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
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Modified Formulation Variable Parameters
dB
dB
fc fc
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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
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Parameter Optimization AlgorithmMid Frequency
Sub-DomainHigh Frequency
Sub-DomainLow Frequency
Sub-Domain
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Parameter Optimization Results
Case dBsdBm f c
Maximum 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
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Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
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Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
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Modified Method with Optimized Parameters
Low Penetration Mixer High Penetration Mixer
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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
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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
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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
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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