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

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


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


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


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


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


WIND

Mean Axial Velocity at x/d = 1.0


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

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

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


• 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


• 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

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

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