Noise from Forced Mixers
-
Upload
4953049530 -
Category
Documents
-
view
217 -
download
0
Transcript of Noise from Forced Mixers
-
7/30/2019 Noise from Forced Mixers
1/41
Noise from Forced Mixers
Funded by the Indiana 21st
Century Research andTechnology Fund
-
7/30/2019 Noise from Forced Mixers
2/41
Correlating RANS Computed
Mean Flow with Forced Mixed
Jets
C. Wright, G. Blaisdell, A. Lyrintzis
School of Aeronautics & Astronautics
Purdue University
-
7/30/2019 Noise from Forced Mixers
3/41
Goals of Project
The primary goal is to develop a greater
understanding of the how noise from
forced mixed jets may be correlated to the
RANS calculated mean flow field.
The ultimate goal is to develop quantitative
correlations that could be used as input for
a semi-empirical model
-
7/30/2019 Noise from Forced Mixers
4/41
Approaches
Careful selection of numerical tools such as theturbulence model and CFD code are veryimportant. Validation should concentrate on adetailed comparison of flow contours rather thanintegrated quantities.
Grid development and validation should likewiseconcentrate on the details of the flow.
Qualitative trends and observations regardingthe relationship between noise data and CFDresults should be investigated before attemptingto quantify the results.
-
7/30/2019 Noise from Forced Mixers
5/41
Internally Forced Mixed Jet
By
pass
Flow
Mixer
Core
Flow
Nozzle
Tail Cone
Exhaust
Flow
Exhaust / Ambient
Mixing Layer
Lobed Mixer
Mixing Layer
-
7/30/2019 Noise from Forced Mixers
6/41
Forced Mixer
H
Lobe Penetration
(Lobe Height)
H:
-
7/30/2019 Noise from Forced Mixers
7/41
3-D Mesh
-
7/30/2019 Noise from Forced Mixers
8/41
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
-
7/30/2019 Noise from Forced Mixers
9/41
Grid Dependence
1.7 million grid points 7 million grid points
Density
Vorticity
Magnitude
-
7/30/2019 Noise from Forced Mixers
10/41
Spalart-Allmaras and and Menter SST at
Nozzle Exit PlaneSpalart SST
Density
Vorticity
Magnitude
-
7/30/2019 Noise from Forced Mixers
11/41
Vorticity Magnitude at Nozzle Exit
( Scale Geometry)
Low Penetration Mid Penetration High Penetration
-
7/30/2019 Noise from Forced Mixers
12/41
Turbulent Kinetic Energy at Nozzle Exit
( Scale Geometry)Low Penetration Mid Penetration High Penetration
-
7/30/2019 Noise from Forced Mixers
13/41
High Penetration Mixer Flowfield
Case is for a high throttlesetting at Mach 0.2
Used Menter SSTTurbulence Model
Good overall agreementwith experiment. TKE is alittle low for X/D = 1.0 andX/D = 2.0. CFD resultstend to be overly sharp anddefined.
CFD and experiment bothshow a substantial amountof interaction between thefree shear layer and thestreamwise vortices.
-
7/30/2019 Noise from Forced Mixers
14/41
Medium Penetration Mixer Flowfield
Case is for a high throttlesetting at Mach 0.2
Used Menter SSTTurbulence Model
The agreement between theCFD and the experiment isabout the same as for thehigh penetration case.
The free shear layer and thestreamwise vortices exist asseparate and distinct flowstructures through at leastX/D = 1.0.
-
7/30/2019 Noise from Forced Mixers
15/41
Experimental Results (1/4 Scale Model)
-
7/30/2019 Noise from Forced Mixers
16/41
Experimental Results (1/4 Scale Model)
-
7/30/2019 Noise from Forced Mixers
17/41
Current State of Project
Finishing up CFD runs. Using WIND and MenterSST turbulence model.
Currently studying noise data along with RANS
results and PIV experiments (including lowpenetration case not shown).
Have identified some interesting trends, and arepreparing more CFD runs to finalize these
comparisons. Specifics of research is being published in a
paper for the AIAA Reno conference (Jan. 2004).
-
7/30/2019 Noise from Forced Mixers
18/41
Development of a Semi-EmpiricalJet Noise Model for Forced Mixer
Noise Predictions
L. Garrison, Purdue University
W. Dalton, Rolls-Royce Indianapolis
A. Lyrintzis and G. BlaisdellPurdue University
-
7/30/2019 Noise from Forced Mixers
19/41
Four-Source Model Comparisons Four-Source method implementation
Predictions for the confluent mixer
Two-Source Model Formulation
Optimization procedure
Optimized results for the 12 lobe mixers
Optimized parameter correlations
Outline
-
7/30/2019 Noise from Forced Mixers
20/41
Practical Configuration Geometry
Secondary Flow
Primary Flow
Flow Mixer
Nozzle Wall
Tail Cone
(Bullet)
Final Nozzle Exit
-
7/30/2019 Noise from Forced Mixers
21/41
Dual Flow Configurations
Four-Source method
developed for acoplanar, coaxial jet
The configuration for the
practical case has a
buried primary flow in a
convergent nozzle with a
center body (tail cone orbullet)
-
7/30/2019 Noise from Forced Mixers
22/41
Based on an Equivalent Coaxial Jet
Approach developed by B. Tester and M.Fisher
Define primary and secondary jets at the
final nozzle exit plane Assumptions
Isentropic flow in the nozzle
Primary and secondary flows do not mix in thenozzle
Static pressure of the two flows at the exitplane are equal
Single Jet Property Calculation
-
7/30/2019 Noise from Forced Mixers
23/41
Single Jet Property Calculation
Jet Areas at the Final Nozzle Exit
GuessAp
CalculateAs
Calculate Mexit
Calculate Pstatic
Iterate until the primary and secondary static
pressures are equal
pns AAA
121
2
2
1
2
2
1
1
1
cs
exit
exit
cs
cs
exit
M
M
M
M
A
A
12
2
11
exit
static
oM
P
PJ
-
7/30/2019 Noise from Forced Mixers
24/41
Four-Source Method Implementation Primary and Secondary Jet Properties
Calculated at the final nozzle exit
Mixed Jet and Effective Jet Properties
1
1TT
1
))(1(1DD
1
1VV
pm
2
1
2pm
2
pm
p
s
p
s
p
s
A
A
V
V
pe
1/22
pe
pe
TT
1DD
VV
7dBe
-
7/30/2019 Noise from Forced Mixers
25/41
Current Prediction Method Comparisons
Four-Source / Single Jet / Experimental
Data Comparisons Confluent Mixer, Low Power Operating Point
ARP876C Method used for all single jetnoise predictions
Bass and Sutherland correction for atmosphericattenuation
Four-Source coaxial jet prediction Based on equivalent coaxial jet properties
Single jet prediction Based on fully mixed flow at the final nozzle exit
-
7/30/2019 Noise from Forced Mixers
26/41
Current Prediction Method Comparisons
-
7/30/2019 Noise from Forced Mixers
27/41
Forced Mixer Experimental Data
Four Mixer Configurations
Confluent Mixer (CFM)
Low Penetration 12 Lobe Mixer (12CL)
Mid Penetration 12 Lobe Mixer (12UM)
High Penetration 12 Lobe Mixer (12UH) Low Power Operating Point
H
-
7/30/2019 Noise from Forced Mixers
28/41
Forced Mixer Experimental Data
-
7/30/2019 Noise from Forced Mixers
29/41
Objective:
Match the experimental data SPL spectrum at all
angles and all frequencies using two single stream
jet sources.
Formulation:
s s s s 1 s0 USPL ( , ) SPL(V ,T ,D , , ) 10lo Bg ( dF , )cf ff f
Single Jet
Prediction
Source
Strength
Spectral
Filter
Variable Parameters:
m m m m 1 m0 DSPL ( , ) SPL(V ,T ,D , , ) 10lo Bg ( dF , )cf ff f
s m
Spectral Filter Cut-off Frequency
, Source StrengtdB hs )d BB (d
cf
Two-Source Model
-
7/30/2019 Noise from Forced Mixers
30/41
Two-Source Model
dB
dB
fc
fc
Variable Parameters
1/3 Octave Band Number 1/3 Octave Band Number
1/3OctaveSP
L[dB]
1/3OctaveSP
L[dB]
Effects of Variations in dB Effects of Variations in fc
-
7/30/2019 Noise from Forced Mixers
31/41
Optimization Procedure For a given geometry and operating condition,optimize the source strength parameters
(dbs, dbm) for a range of cut-off frequencies
Find the set of optimized parameters that
minimize the prediction error for all operating
conditions
Correlate the final set of parameters to the
changes in the mixer design
Two-Source Model Optimization
-
7/30/2019 Noise from Forced Mixers
32/41
Optimization Challenges
Optimum Criterion Maximum Error
Average Error
Weighted Error
Solution Non-Uniqueness Local Minima
Non-Linear Behavior
Optimization Tools
Nonlinear Least Squares
MATLAB: lsqnonlin (LevenbergMarquadt Optimization
Method )
Two-Source Model Optimization
-
7/30/2019 Noise from Forced Mixers
33/41
Two-Source Model Optimization
15 Microphone locations (90 to 160 in 5 increments)
1 Sound Pressure Level (SPL) spectrum per microphone
27 Frequency Bands per spectrum (1/3 Octave Bands)
405 SPL values per data point
Microphone Locations
Jet80observer
J
r
D
-
7/30/2019 Noise from Forced Mixers
34/41
Two-Source Model Optimization
Optimum Criterion
Based on a OASPL type weighting
At each observer angle:
Weighted error values:
exp exp
max
0.1 SPL , SPL ,
, 10i if f
w iE f
, , , ,w exp pred Error f E f SPL f SPL f
-
7/30/2019 Noise from Forced Mixers
35/41
Two-Source Model Results Test Case
Low Penetration Mixer Low Power Operating Point
Two-Source Model Upstream Source: Secondary Jet
Downstream Source: Mixed Jet
Prediction
Method
Maximum
Error [dB]
Average
Error [dB]
Weighted
Error [dB]
Four-Source 13.18 2.56 0.41
Single Jet 12.02 2.53 0.64
Two-Source 8.35 1.29 0.36
O ti i d T S R lt
-
7/30/2019 Noise from Forced Mixers
36/41
Optimized Two-Source Results
-
7/30/2019 Noise from Forced Mixers
37/41
Optimized Two-Source Results
-
7/30/2019 Noise from Forced Mixers
38/41
-
7/30/2019 Noise from Forced Mixers
39/41
Current jet noise predictions do notaccurately model the noise from jets withinternal forced mixers
Forced mixer jet noise can be modeled by
a combination of two single jet sources Optimized Two-Source model source
strengths and cut-off Strouhal numbersappear to correlate linearly with theamount of lobe penetration
Summary
-
7/30/2019 Noise from Forced Mixers
40/41
Fisher, M.J., Preston, G.A., and Bryce, W.D., A Modelling of the
Noise from Simple Coaxial Jets Part I: With Unheated PrimaryFlow, Journal of Sound and Vibration, 209(3):385-403, 1998
Fisher, M.J., Preston, G.A., and Mead, C.J., A Modelling of the
Noise from Simple Coaxial Jets Part II: With Heated Primary Flow,
Journal of Sound and Vibration, 209(3):405-417, 1998
ARP87C: Gas Turbine Jet Exhaust Noise Prediction, Society ofAutomotive Engineers, Inc., November, 1985.
Bass, H.E., Sutherland, L.C., Zuckerwar, A.J., Blackstone, D.T., and
Hester, D.M., Atmospheric Absorption of Sound: Further
Developments, Journal of the Acoustical Society America,
97(1):680-683, 1995
References
-
7/30/2019 Noise from Forced Mixers
41/41
Two-Source Model Optimization
SPLexp - SPLpredSPLexpSPLexpmax