This was my final presentation for my Aeroacoustics course at Purdue

1. A Review of an Empirical Turbine
Noise Prediction Method
Noise Prediction Method
Kyle Grose
AAE 615

2. Objective
Investigate the physics behind the empirical turbine
noise prediction method presented by Bruce Morin
and Oliver Atassi at the 2008 AARC Turbine Noise
Workshop
Compare against the popular G.E. empirical turbine
Compare against the popular G E empirical turbine
prediction
Investigate how the Morin‐Atassi Prediction Model
g
compares against measured data.

3. Why is Turbine Noise Important?
As noise reduction technology has progressed, once dominant noise
sources that used to mask lesser noise sources have become
suppressed.
Lesser noise sources, such as the turbine, are now significant
contributors to community noise especially in the approach
condition
Typical Engine Noise Component Distribution
Figure from Mathews Combustion
Approach Condition And Turbine Noise Work at Pratt &Whitney
Presented at Core Noise Workshop in Phoenix, AZ
February 2003

4. Turbine Noise Generation Mechanisms
There are two types of generation mechanisms for turbine noise:
potential field interactions and blade‐wake interactions
Figure from Morin and Atassi An Empirical Model
For Turbine Noise Prediction presented at
AARC Turbine Noise Workshop in Vancouver, BC
May 2008

5. Potential Field Interactions
Potential Field Interactions
Solid bodies in free stream flow create potential fields
Potential fields can travel and interact with other objects or other flow
j
phenomena
Flow passing by a stator will cause a potential field that can travel upstream
and impinge upon the upstream rotor row causing pressure fluctuations
therefore generating noise
th f ti i
Potential field tends to decay rapidly and because of blade spacing in
turbine stages the potential fields cause weak excitations
Blade‐wake Interactions are often seen as the dominant noise
generation mechanism of turbine noise

6. Blade‐wake Interactions
Blade‐wake interactions occur when viscous wakes are shed off of
either rotor blades or stator vanes and interact with either stator or
rotor blades next to it.
The wakes impinging on the blade cause unsteady lift forces on the blade
Th k i i i th bl d t d lift f th bl d
which generates noise
Viscous wakes do not decay as fast as a potential field. Viscous wakes are
the dominant noise source.
the dominant noise source
Figure from M.J.T. Smith Aircraft Noise,
Cambridge University Press, New York, 1989

7. M i At i M d l
Morin‐Atassi Model
OAPWL Due to Airfoil Row
Morin‐Atassi model assumes turbine noise is
dominated by blade wakes
For a single row of airfoils the sound power produced is
F i l f i f il h d d d i
dependent on the following:
Sound Power
Upwash
Annulus Area Pressure Loss Factor
Temperature
Spacing/Chord Ratio
Pressure
Duct Mach Number
p, q, and r are empirical constants

8. M i A iM d l
Morin‐Atassi Model
Where do the Parameters Come From
Even though this is an empirical model there needs to
be rationale behind the parameters chosen
Curle’s Equation
Pressure is dependent upon the force
What is the force due to a viscous wake on a blade?

9. Unsteady Forces on a Blade Due to
Unsteady Forces on a Blade Due to
Viscous Wake
Kemp and Sears wrote a paper “Unsteady Forces Due
to Viscous Wakes in Turbomachinery” (1955)
Described the upwash on a blade due to a viscous wake
as dependent upon drag coefficient (CD) and
spacing/chord ratio (s/b) in an incompressible flow
h d ( b) bl fl
Lift is dependent upon the upwash
p p p
Therefore unsteady force dependent upon CD and
(s/b)
( b)

10. U d F Bl d
Unsteady Forces on a Blade
Kemp and Sear Upwash Equation & Lift Equation
Upwash is Dependent upon
CD and (s/b)
( / )
Upwash
Lift
Lift is Dependent upon CD and (s/b)

11. M i A i
Morin‐Atassi
Kemp and Sears Contribution
Simplified CD = Pressure Loss Coefficient (ξ)
W = P*A Where Do These
(assuming free‐field) Come From?
Kemp‐Sear’s Lift Model

12. U d F Bl d
Unsteady Forces on a Blade
Osborne Compressible Model
Osborne wrote a paper “Compressible Unsteady
Interactions Between Blade Rows” (1972)
Compressibility is Important. There is significant
C ibili i I Th i i ifi
impact on lift fluctuations by varying Mach number
Lift Model for Compressible Flow
Upwash from Sears‐Kemp
V = Mc
p = ρRT
c = sqrt(γRT )
Lift dependent upon T1/2, p, M, (s/b), and ξ

13. M i At i M d l
Morin‐Atassi Model
OAPWL for Turbine Stage
OAPWL before described for a single airfoil, the
OAPWL for a turbine stage is the following:
Upstream Vane Wake/Blade
Interaction
Blade Wake/Downstream Vane
Interaction

14. OAPWL f T bi S
OAPWL for Turbine Stage
Additional Terms
Along with the superposition of blade‐wake interactions, there is also
the inclusion of K,C, and τ
K – Modal Cut‐On/Cut‐Off Parameter
K Modal Cut On/Cut Off Parameter
Not all modes creating from interaction couple with duct mode
If mode is “cut‐on” K = 1, if “cut‐off” K=0.
τ – Transmission Loss Coefficient
Modes propagating downstream will interact with other blade row
Energy may get scattered
Transmission coefficient is the portion of the initial energy generated
T i i ffi i t i th ti f th i iti l t d
by the blade‐wake interaction that makes it to the farfield
C – Additional Empirical Coefficient
p

15. OAPWL
Discrete Frequency and “Haystack” Components
OAPWL represents both the discrete frequency and
broadband level
Two components to turbine noise
T bi i
Discrete Frequency (turbine tones)
Broadband (Haystacks)
Figure from Mathews Combustion
And Turbine Noise Work at Pratt & Whitney
Presented at Core Noise Workshop in Phoenix, AZ
February 2003

16. Turbine Tones and Haystack Generation
Turbine Tones
Turbine tones occur because of the periodic
interaction of the rotating rotor blades with
the stator vanes
h
Fig. 1
Fi
Haystacks
Haystacks occur external to the turbine
As the tone propagates out the exhaust, it
p p g ,
moves through the fan/ambient shear layer
Turbulence in this layer causes the tone to
modulate and spectrally spread out.
“Haystacks” are generated Fig. 2
Figure 2 from Mathews Combustion
And Turbine Noise Work at Pratt & Whitney
Figure 1 from M.J.T. Smith Aircraft Noise, Presented at Core Noise Workshop in Phoenix, AZ
Cambridge University Press, New York, 1989 February 2003

17. OAPWL
Tonal and Haystack Level
Tonal level is found by directly applying the OAPWL model
Because of complexity of the tonal modulation, an
envelope was empirically derived to describe the haystack
spectral shape.
Measured data was analyzed and a mean curve was created for
the Haystack envelope
Figure from Morin and Atassi An Empirical Model
For Turbine Noise Prediction presented at
AARC Turbine Noise Workshop in Vancouver, BC
May 2008

18. G.E. Empirical Model
Kazin and Matta formulated an empirical model for
turbine prediction in the 1970’s. It is still popular and
found in many papers.
found in many papers
SPL prediction for both tonal and broadband levels
Have another equation for the peak SPL level (tonal
component)
Subtract the peak SPL from the OASPL to find
p
broadband level

19. Morin‐Atassi vs. Kazin‐Matta
Morin‐Atassi
X X
Kazin‐Matta
X
ΔT / T = 1 − (1 / ξ )^ (γ − 1/γ )
Kazin Matta
Kazin‐Matta recognized significance in blade spacing but did not include it in
their model
Kazin‐Matta rely on the relative blade velocity as opposed the duct Mach
number in the Morin‐Atassi model
Even though they both attempt to predict the same thing, there are major
Even though they both attempt to predict the same thing there are major
differences between the two

20. M i A i
Morin‐Atassi
Prediction vs. Measured
Used Pratt & Whitney measured data to obtain
empirical constants and “haystack” spectral shapes
Inferred transmission loss coefficients for the various
I f d i i l ffi i f h i
turbine stages
Obtained good
correlation between
the measured and
predicted results
Figure from Morin and Atassi An Empirical Model
For Turbine Noise Prediction presented at
AARC Turbine Noise Workshop in Vancouver, BC
May 2008

21. Comparison of Different Empirical
Comparison of Different Empirical
Methods
In 1975, Mathews and Nagel conducted a review of turbine
noise prediction methods used at NASA, G.E., Pratt &
Whitney and Rolls‐Royce
Discovered that the prediction for a single engine varied
d h h d f l d
widely across the groups involved in the review
Though the Morin‐Atassi
g
method might correlate
well with P&W data, it
might not work as well
when predicting turbine
noise for a different engine
Figure from Mathews, et al “Review of Theory
and Methods for Turbine Noise Prediction,”
AIAA Paper No. 75‐540, 1975

22. Conclusions
Morin‐Atassi method appears to have strong ties to
blade‐wake interaction theory
Before a definitive conclusion can be made on the
B f d fi i i l i b d h
accuracy of the model, the model needs to be
validated against a variety of engines.
validated against a variety of engines

23. References