development of low-noise aircraft engines anastasios lyrintzis school of aeronautics &...

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