Lennart S. Hultgren and Jeffrey H. Miles

Glenn Research Center, Cleveland, Ohio

Noise-Source Separation Using Internal

and Far-Field Sensors for a Full-Scale

Turbofan Engine

NASA/TM—2009-215834

November 2009

AIAA–2009–3220
NASA STI Program . . . in Profi le

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA Scientifi c and Technical Information (STI) 

program plays a key part in helping NASA maintain 

this important role.

The NASA STI Program operates under the auspices 

of the Agency Chief Information Offi cer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NASA Aeronautics and Space Database and 

its public interface, the NASA Technical Reports 

Server, thus providing one of the largest collections 

of aeronautical and space science STI in the world. 

Results are published in both non-NASA channels 

and by NASA in the NASA STI Report Series, which 

includes the following report types:

• TECHNICAL PUBLICATION. Reports of 

completed research or a major signifi cant phase 

of research that present the results of NASA 

programs and include extensive data or theoretical 

analysis. Includes compilations of signifi cant 

scientifi c and technical data and information 

deemed to be of continuing reference value. 

NASA counterpart of peer-reviewed formal 

professional papers but has less stringent 

limitations on manuscript length and extent of 

graphic presentations.

• TECHNICAL MEMORANDUM. Scientifi c 

and technical fi ndings that are preliminary or 

of specialized interest, e.g., quick release 

reports, working papers, and bibliographies that 

contain minimal annotation. Does not contain 

extensive analysis.

• CONTRACTOR REPORT. Scientifi c and 

technical fi ndings by NASA-sponsored 

contractors and grantees.

• CONFERENCE PUBLICATION. Collected 

papers from scientifi c and technical 

conferences, symposia, seminars, or other 

meetings sponsored or cosponsored by NASA.

• SPECIAL PUBLICATION. Scientifi c, 

technical, or historical information from 

NASA programs, projects, and missions, often 

concerned with subjects having substantial 

public interest.

• TECHNICAL TRANSLATION. Englishlanguage translations of foreign scientifi c and 

technical material pertinent to NASA’s mission.

Specialized services also include creating custom 

thesauri, building customized databases, organizing 

and publishing research results.

For more information about the NASA STI 

program, see the following:

• Access the NASA STI program home page at 

http://www.sti.nasa.gov

• E-mail your question via the Internet to help@

sti.nasa.gov

• Fax your question to the NASA STI Help Desk 

at 443–757–5803

• Telephone the NASA STI Help Desk at

 443–757–5802

• Write to:

 NASA Center for AeroSpace Information (CASI)

 7115 Standard Drive

 Hanover, MD 21076–1320
Lennart S. Hultgren and Jeffrey H. Miles

Glenn Research Center, Cleveland, Ohio

Noise-Source Separation Using Internal

and Far-Field Sensors for a Full-Scale

Turbofan Engine

NASA/TM—2009-215834

November 2009

AIAA–2009–3220

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

15th Aeroacoustics Conference (30th AIAA Aeroacoustics Conference)

cosponsored by AIAA and CEAS

Miami, Florida, May 11–13, 2009
Acknowledgments

This work was supported by the NASA Fundamental Aeronautics Program Subsonic Fixed Wing Project. The NASA 

Fundamental Aeronautics Program has the principal objective of overcoming today’s national challenges in air

transportation. The work herein is part of a NASA-internal and NASA-sponsored external research effort aimed

at the development of component-based aircraft noise-prediction capability and tools. The reduction of

aircraft noise is critical for enabling the anticipated large increase in future air traffi c.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5285 Port Royal Road

Springfi eld, VA 22161

Available electronically at http://gltrs.grc.nasa.gov

This work was sponsored by the Fundamental Aeronautics Program 

at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by technical management. 
Noise-source separation techniques for the extraction of the sub-dominant combustion noise from the total

noise signatures obtained in static-engine tests are described. Three methods are applied to data from a static,

full-scale engine test. Both 1/3-octave and narrow-band results are discussed. The results are used to assess the

combustion-noise prediction capability of the Aircraft Noise Prediction Program (ANOPP). A new additional

phase-angle-based discriminator for the three-signal method is also introduced.

I. Introduction

Noise generated in the jet engine core, by sources such as the compressor, combustor, and turbine, can be a

significant contribution to the overall noise signature at low-power conditions, typical of approach flight. At high

engine power during takeoff, jet and fan noise have traditionally dominated over core noise. However, current design

trends and expected technological advances in engine-cycle design as well as noise-reduction methods are likely to

reduce non-core noise even at engine-power points higher than approach. The result of such changes will be to elevate

the importance of turbomachinery core noise. Also, future airport regulations may force further noise reduction at the

approach flight conditions, thus emphasizing the need for reductions in core noise.

Ref. 1 and Ref. 2, in the collection by Ref. 3, summarize the status and understanding of turbomachinery contributions to flight vehicle noise at the beginning of the 1990’s. Whereas jet and fan noise research and understanding

have progressed; the state of the art in core-noise prediction has not been equally advanced. Low frequency core noise

up to about 1 kHz, particularly in aft quadrants, is generally attributed to combustion noise. Turbine broadband and

discrete-tone noise normally falls in the frequency range of several kHz and above. The discrete tones are caused by

stator/rotor wakes interacting with downstream rotors/stators and potential fields interacting with upstream and downstream blade rows. Internal turbine broadband noise is caused by rotor/stator lift fluctuations due to flow turbulence.

However, there is also a significant broadband component in the far field associated with scattering of the discrete

turbine tones by the jet shear layer(s). This so called hay-stacking effect usually dominates the broadband noise generated within the turbine. Compressor noise occurs mainly in the forward direction as discrete tones at the first stage

blade-passing frequency (kHz) or as a compressor-disk tone at the compressor shaft frequency (several hundred Hz).

The compressor is commonly not considered a significant noise source for today’s high-bypass turbofan engines. The

present paper is concerned with the combustion noise component of the core noise.

Combustion noise is either of the direct or indirect type. The unsteady combustion process is the source of pressure,

entropy, and vorticity fluctuations. The frequency scale is set by the unsteady combustion process and falls in the

range of 400–500 Hz. A fraction of the pressure disturbances are acoustic pressure fluctuations with the balance

being hydrodynamical unsteadiness. The former is what is referred to as direct combustion noise. Its spectrum is

modified by the combustor geometry as well as pressure feedback on the unsteady combustion process itself. The

direct combustion noise is reduced due to transmission effects during its propagation through the turbine stages. The

combustor entropy (temperature) fluctuations are convected downstream with the local mean velocity and get converted

to acoustic pressure fluctuations in the turbine and other regions of rapid flow change. This is the indirect process of

Abstract 

NASA/TM—2009-215834 1

Noise-Source Separation Using Internal and Far-Field Sensors 

for a Full-Scale Turbofan Engine 

Lennart S. Hultgren and Jeffrey H. Miles 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 
turbomachinery combustion noise generation. This is potentially a very effective mechanism and occurs at all turbine

stages. The indirect noise occurs in the same basic frequency range as the direct one, but their spectral-distribution

shapes could be quite different. The relative importance of direct and indirect combustion noise is still an unresolved

issue. Any quasi-steady temperature streak ingested by the turbine would have an effect similar to a fan/compressor

inlet disturbance/vortex and would generate noise in the blade-passing frequency range. This possibility should be

classified as a turbine self-generated noise mechanism.

The direct measurement of turbofan-engine combustion noise is difficult because of the presence of jet noise in the

frequency range of interest. Since in-flight effects reduce jet noise more than combustion noise, combustion noise can

be a significant contributor to aircraft approach noise but masked by jet noise under the corresponding static-engine

test condition. To overcome this obstacle, researchers4–13 developed coherence techniques utilizing engine-internal as

well as far-field measurements to identify the far-field combustion noise component. Modal analyses14–17 were also

carried out to determine the source and propagation characteristics of combustion noise.

In this paper, a discussion of noise-source separation techniques for application to engine test-stand data and an

assessment of a current prediction method applied to a static-engine test will be presented.

II. Coherence Techniques

Figure 1 illustrates a typical three-sensor arrangement for a measurement utilizing correlation/coherence techniques to determine the combustion-noise component of the far-field noise signature. Measurement station ’C’ is

C

uv hvw

+ + +

m(t)

u(t)

x(t) n(t) y(t)

w(t)

o(t) z(t)

v(t)

T F

h

Figure 1. Schematic Diagram for Coherence-Technique Measurements

located inside the combustor, station ’T’, if present, is typically located in the core tail pipe, and station ’F’ is located

in the far field. The signals u(t), v(t), and w(t) represent the acoustic combustion-noise signal at the three stations as

functions of time t. These ‘desired’ signals cannot be directly obtained by themselves because of the presence of the

random uncorrelated ‘noise’ signals m(t), n(t), and o(t) at the different stations. However, the downstream signals

v(t) and w(t) are uniquely determined by the impulse-response functions huv and hvw and the previous-station signals u(t) and v(t), respectively. The measurable signal, x(t), y(t), or z(t), at each station is the sum of the ‘desired’

and corresponding ‘noise’ signals. The signals m(t), n(t), and o(t) can be taken as mutually uncorrelated as well as

uncorrelated with the combustion-noise signal u(t), v(t), or w(t) at all the stations. The signal m(t) is to a large extent

caused by hydrodynamical pressure fluctuations (pseudo sound) in the combustor and possibly also higher acoustic

modes present in the combustor but cut-off in the downstream tail pipe and can potentially be quite large. The signals

n(t) and o(t) are mainly due to acoustic pressure fluctuations from other noise sources and particularly o(t) can be

dominant in the frequency range of interest due to the presence of jet noise. The goal is to determine the one-sided

auto-spectrum Gww(f), with f denoting frequency, i.e., the combustion-noise component of the total far-field noise

signature Gzz(f). The diagnostic technique is commonly referred to as the coherent-output-power method when only

one sensor inside the engine is utilized in combination with the far field microphone and will be discussed next. Then

the three-signal technique will be addressed.

A. Coherent-Output-Power Method

Ref. 4 describes the basic formulation for the coherent-output-power method. If the sensor inside the combustor and

one in the far field (Fig. 1) are used in this technique, it follows that the coherent combustion-noise spectrum is given

by

Gww(f) = |Guw(f)|

2

Guu(f)

=

|Gxz(f)|

2

Guu(f)

=

|Gxz(f)|

2

Gxx(f) − Gmm(f)

≈

|Gxz(f)|

2

Gxx(f)

= γ

2

xz(f)Gzz(f) (1)

NASA/TM—2009-215834 2
m(t)

sw

hsv

hsu

+

w(t) z(t)

o(t)

+

v(t) y(t)

n(t)

s(t)

+

u(t) x(t)

h

Figure 2. General Schematic Diagram for Three-Signal-Technique

regardless of the output noise Goo(f). Gαα, Gαβ, γαβ = |Gαβ|/

p

GααGββ denote the one-sided auto-spectrum,

cross-spectrum and coherence of the signals α and β, where α and β are dummy indexes. Note that the measured

Gxx is a positive-biased estimate for the unknown input spectra Guu and, consequently, the second-to-last step in

Eq. (1) is only a valid approximation when the unknown Gmm can be assumed to be small compared to Gxx. In

view of the certain presence of nonpropagating pressure fluctuations in the combustor, i.e., Gmm = 0 6 , Eq. (1) is

quite likely to underpredict the actual coherent output spectrum. Furthermore, the spectra are estimated using a finite

observation time and, consequently, the signal z(t) needs to be time shifted to account for a propagation-time delay in

the computation of the cross-spectrum Gxz in order to yield a meaningful result.

Karchmer and colleagues5–8 showed successful application of coherence techniques to measurements obtained

using a static AVCO-Lycoming YF102 turbofan engine instrumented with internal as well as far-field microphones.

A typical result showed the combustion coherent output power in the 120 degree direction (aft quadrant) to roughly

have a dome-shaped spectral distribution in the range 0 to 200 Hz with the peak at about 125 Hz. At 43 percent engine

speed, the peak was about 6 dB lower than the peak of the total far-field spectrum located at 120 Hz. The dome edges

were about 20 dB lower at 40 Hz and 200 Hz. Similar coherence-function results also were obtained from static tests

of Pratt & Whitney JT15D9and General Electric CF6-5010 engines.

B. Three-Signal-Coherence Method

Chung18 developed a three-signal coherence technique for microphone flow-noise rejection. Figure 2 shows the general block diagram for this method, where s(t) is the source signal and, as before, u(t), v(t), and w(t) are the coherent

signals, m(t), n(t), and o(t) represent mutually uncorrelated noise, and x(t), y(t), and z(t) are the measurable signals.

The coherent auto-spectra at the three measuring stations in Fig. 2 are given by

Guu(f) = |Gxy(f)||Gxz(f)|

|Gyz(f)|

, (2a)

Gvv(f) = |Gxy(f)||Gyz(f)|

|Gxz(f)|

, (2b)

Gww(f) = |Gxz(f)||Gyz(f)|

|Gxy(f)|

. (2c)

However, the three-signal method also applies to the situation shown in Fig. 1, where the measuring stations and,

consequently, the signals u(t), v(t), and w(t) are ‘sequential’ rather than ‘parallel-output’ measurements as in Fig. 2.

Krejsa11 considered the situation depicted in Fig. 1 and obtained Eq. (2c) as his far-field result. The three-signal

coherence technique used by Krejsa11–13 eliminates the bias error in the coherent combustion-noise measurements due

to engine-internal nonpropagating pressure fluctuations.

The strength of the three-signal method is that it involves only measured cross-spectra. The measured crossspectra are affected by extraneous noise only if this noise correlates between measurement locations. This can often

NASA/TM—2009-215834 3
be avoided by an appropriate spatial separation of the sensors involved and the three-signal method then provides

unbiased estimates of the coherent auto-spectra. In contrast, measured auto-spectra will always include a positive

definite contribution from the extraneous noise.

Krejsa compared results obtained using the earlier coherent-output-power and the new three-signal methods for

YF102,11–13 JT15D,12 and CF6-5012 static-engine tests.8–10 He found that the older method consistently underpredicted the combustion noise spectra by several dB at low engine speeds and of the order of 10 dB at a high engine

speed. Ref. 13 also discussed two other techniques19, 20 for isolating the combustion noise using only far-field measurements, one of which introduced a model for the jet noise.20 The three-signal coherence technique also was used

by Shivashankara21 to study core noise in a Pratt & Whitney JT9D engine.

The three-signal coherence data for the YF102, JT15D, and CF6-50 static-engine tests were further examined

and correlated by von Glahn and Krejsa.22 The suggested correlations utilized one, two, and four spectral segments

to capture perceived peaks in the far-field core-noise spectra. Generally, the sound pressure levels at the singlesegment spectrum peak, the higher-frequency two-segment spectrum peak, and the two higher-frequency four-segment

spectrum peaks scaled with a heat-release parameter as in the semi-empirical model used in ANOPP,23, 24 whereas the

low-frequency segments scaled with the combustor exit velocity to the fourth power. They stated that the four-segment

spectra provided the best overall fit to the data, but also that the two-segment spectrum appeared to be a reasonable

representation of the combustion noise. However, the data comparisons do not generally show the expected 3 dB

increase at the intersection of the multi-segment spectra, which renders their conclusions less clear. Nevertheless, their

results show that the assumption of a single-peaked far-field combustion-noise spectrum with the peak fixed at 400

Hz, as used in ANOPP, can be questioned.

It is also possible to separate core noise from jet noise using three far-field microphones since each would pick up

correlated core noise and uncorrelated external noise from the jet.21, 25 As long as the spatial (polar angle) separation

of the microphones is large enough, the jet noise at each location can be assumed to be mutually uncorrelated and

Eq. (2) would apply. Ref. 25 analyzed data from a Honeywell TECH977 static test26 using a three-signal far-field

method, among other multiple-microphone signal-processing techniques.27 They found that the method worked well

in frequency regions where a single engine-internal source was dominant. The method did not perform well for

frequencies where multiple self-correlated internal noise sources were of comparable magnitude, e.g. in the relatively

limited frequency range where combustion noise and turbine-broadband noise overlapped.

III. Results

A. Static-Engine Test Data

Data obtained during the NASA/Honeywell EVNERT26 program is used to assess the combustion-noise prediction

capability of ANOPP. The engine-internal instrumentation in EVNERT configuration 35 included high-temperature

pressure sensors with air cooling in a combustor ingnitor port (CIP1) and at the turbine exit (T551 and T552). Pressure

time histories at the internal sensors CIP1 and T551 and the aft-quadrant far-field microphone located in the 130o

polar direction (measured from the inlet) are used herein. The EVNERT data acquisition system had a sampling rate

of 65,536 Hz and a duration of about 70 s, leading to time histories with just over 4.5 million data points. Each time

series is here analyzed using an FFT length of 8192 points (corresponding to an 8 Hz frequency resolution or bin

width), Hanning windowing, and a 50 percent data-segment overlap. The narrow-band auto spectra, resulting from

M = 1117 averages, are then summed up to yield the corresponding 1/3-octave SPL. The 1/3-octave far-field total

SPL results are in full agreement with the Honeywell provided 1/3-octave SPL data.

B. Source-Separation Implementation

Using the two- and three-signal coherence techniques described in sections IIA and IIB, the combustion-noise component Gww(f) of the far-field noise signature Gzz(f), see Fig. 1, can be determined by

Gww(f) = γ

2

αz(f)Gzz(f), α = x, y , (3a)

where either the combustor sensor CIP1 (α = x) or the turbine-exit sensor T551 (α = y) is used in combination with

the 130ofar-field microphone, and/or by

Gww(f) = γxz(f)γyz(f)

γxy(f)

Gzz(f), (3b)

NASA/TM—2009-215834 4
where signals from CIP1 sensor, T551 sensor and the far-field microphone are utilized. These three choices will be

referred to as the ‘2s-cip1’, ‘2s-t551’, and ‘3s’ cases/methods in what follows. Equation (3a) is simply a restatement

of Eq. (1) allowing for a choice in engine-internal signal and Eq. (3b) follows directly from Eq. (2c) by using the

definition of coherence.

From a purely theoretical point of view, 0 6 γαβ 6 1, with γαβ = 0 meaning that the two signals α(t) and β(t)

are completely uncorrelated and γαβ = 1 indicating perfectly correlated signals. In practice, only estimates γˆαβ of

the coherence can be obtained using finite data series. The estimated coherence will, in fact, be nonzero even for

completely uncorrelated signals,28, 29 i.e., only the interval

ǫ < γˆαβ 6 1 (4)

is meaningful, where

ǫ

2 = 1 − (1 − PI )1/(Ns−1) (5)

is the PI -percent confidence interval if the true γ

2

αβ is zero and Ns is the number of independent data segments

used in obtaining γˆ

2

αβ. Welch30 showed, in the context of estimating auto power spectra, that Ns can be replaced by

9M/11, where M is the number of 50-percent-overlapped segments used in the analysis. Miles29 suggested that a

better estimate for the coherence threshold value, or noise floor, ǫ can be obtained by purposely unaligning the two

time series. That is, a time delay is deliberately introduced to ensure that the two resulting finite time series are

uncorrelated. The estimated unaligned coherence does not depend on any particular assumptions about the underlying

statistical properties of the time series and accounts for any data-segment overlap and algorithms used in the analysis.

The unaligned result captures the coherence of any discrete tones present in the signals and also provides an estimate of

the minimum observable broadband coherence. Miles29, 31 found that Eq. (5) with Ns = M provided a good estimate

of the noise floor. Following Miles,29, 31 the estimated coherence threshold for the present study is ǫ = 0.0518. If the

estimated coherence exceeds the threshold the two time series are coupled. If it is less than the threshold the signals

are random and appear independent for that particular number of samples/segments.

In the two-signal (coherent-output-power) method calculations carried out here, the estimated coherence γˆαz(f)

is replaced by the threshold value ǫ if it falls below that value for a particular narrow-band frequency. That is, the

estimated narrow-band combustion-noise component Gˆww(f) is simply set to ǫ

2Gˆ

zz(f) for the frequency in question.

Otherwise it is given by the equivalent of Eq. (3a). The narrow-band estimate is then frequency summed to yield the

corresponding 1/3-octave result for the far-field combustion-noise component.

Mathematically, it follows from Eq. (4) that

ǫ

2 < γˆxzγˆyz/γˆxy < ǫ−1

. (6)

The upper limit of this inequality is an unphysical result in view of Eq. (3b) and the fact that Gˆww cannot be lager

than Gˆ

zz. Clearly, an additional discriminator is needed to ensure a physically realistic three-signal combustion-noise

estimate. This is provided by the following necessary condition for Eq. (2) to be valid:

Θ ≡ arg[Gxzconj(Gyz)/Gxy] = 0 . (7)

The standard deviation (in radians) of the estimate for the cross-spectrum phase angle θαβ = arg(Gαβ) is given by4, 31

σαβ = sin−1

q

(1 − γ

2

αβ)/2γ

2

αβNs . (8)

Note that the standard deviation is zero for perfectly correlated signals and increases as the coherence is diminished.

Consequently, in the three-signal method calculations carried out here, the estimated narrow-band combustion-noise

component for a particular narrow-band frequency Gˆww(f) is set to ǫ

2Gˆ

zz(f) if, any of the estimated coherence

values, γˆxz, γˆyz, or γˆxy, fall below the threshold value ǫ, or if the estimated phase angle Θˆ > σˆxz + ˆσyz + ˆσxy, where

σˆαβ = sin−1

q

[1/ max(ˆγ

2

αβ, ǫ2) − 1]/2Ns. Otherwise it is given by the equivalent of Eq. (3b). The narrow-band

estimate is then summed up to yield the corresponding 1/3-octave result for the combustion-noise contribution to the

far-field noise signature. Note that the discriminator procedure based on the phase angle is a new method.

C. Statistical Uncertainty

The textbook by Bendat & Piersol4 gives a summary of statistical random error formulas for single input/output

systems. From these, it follows that the statistical uncertainties for the present coherent-power-method estimates are

NASA/TM—2009-215834 5
given by

Er[Gˆ

zz(f)] = 1

√

Ns

, (9a)

Er[Gˆ

αβ(f)] = 1

γαβ√Ns

, (9b)

Er[ˆγ

2

αβ(f)] =

√

2[1 − γ

2

αβ(f)]

γαβ(f)

√

Ns

, (9c)

Er[Gˆww(f)] = [2 − γ

2

αz(f)]1/2

γαz(f)

√

Ns

, α = x, y , (9d)

where Er denotes the relative error/uncertainty. To evaluate these, the unknown true coherence γαβ is replaced by

its computed value γˆαβ. Equation (9a) is the well known result that the uncertainty in an auto-spectrum estimation

decreases as the inverse square root of the number averages used. This result shows that for the present computations,

the statistical uncertainty of the total-noise auto spectrum is about 3.3 percent, which is an order of magnitude smaller

than the 33 percent (1.25 dB) estimated measurement uncertainty. Equation (9c) shows that the uncertainty in the

coherence vanishes as the signals become correlated. This indicates that computed coherence values are more accurate

than the quantities used in its evaluation. It is also important to realize that the estimate (9d) also assumes that the input

signal (see Fig. 1) does not contain a noise component, i.e., m(t) ≡ 0 when α(t) = x(t) or n(t) ≡ 0 for α(t) = y(t).

Using logarithmic differencing, it follows that the statistical uncertainty in the three-signal method can be expressed as

Er[Gˆww(f)] 6

1

2

Er[ˆγ

2

xz(f)] + 1

2

Er[ˆγ

2

yz(f)] + 1

2

Er[ˆγ

2

xy(f)] + Er[Gˆ

zz(f)] . (10)

For perfectly correlated signals, both Eqs. (9d) and (10) reduce to the equivalent of Eq. (9a). As the signals become

uncorrelated, Eqs. (9d) and (10) indicate a relative uncertainty of p

2/ǫ2Ns and 3

2

p

2/ǫ2Ns, respectively. Using the

present number of data segments, the statistical uncertainty in the combustion-noise estimate is about 3 percent when

the signals are highly correlated and becomes roughly 80 and 120 percent (depending on source-separation method) as

they become poorly correlated. This suggests that the three-signal method is less robust than the two-signal methods

when the coherence is small.

D. Combustion-Noise 1/3-Octave Results

The far-field signal z(t) must be time shifted to account for the physical propagation delay between the engine-internal

sensors and the 130o microphone. Following Miles,31 the corresponding time series is shifted by 5800 points, which

corresponds to an 88.5 ms time delay. Figure 3 shows the results of the source-separation procedures at the four

engine power settings of 48, 60, 71, and 87 percent corrected fan speed (flight-idle, approach, cutback, and takeoff

conditions) for the 1/3-octave center frequency range of 20 to 1000 Hz. The solid lines represent the ANOPP 1/3-

octave SPL predictions for the total (dark-grey) and combustion (red-brown) noise. The symbols correspond to results

computed from the experimental time histories as described above. The black squares, labeled Gzz, represent the

total noise signature, which is reasonably well predicted by the ANOPP results. The grey squares, labeled NOP,

correspond to the threshold value for the coherent output power and any combustion-noise result below these values

would not be meaningful using the present number of data segments and source-separation techniques. The blue, red,

and green squares correspond to the combustion noise detected using the three methods ‘2s-cip1’, ‘2s-t551’, and ‘3s’,

respectively.

By comparing the results for the three different cases, the effect of the positive-bias error inherent in the two-signal

method, not unexpectedly, leads to an underprediction of the peak value when the combustor-internal sensor CIP1 is

used. The two-signal method utilizing the turbine-exit sensor T551 and the three-signal method both clearly detect a

single combustion noise peak, but indicate that the 400 Hz combustion-peak location used in ANOPP is not adequate

for this particular engine. The peak occurs at least two 1/3-octave bands lower. The ANOPP peak value also appears

to be at least 3 dB too low.

Harper-Bourne et al.,32 in their combustor-noise study of the large ANTLE research engine, have suggested that

the difference between the two-signal results using combustor-internal and exit-pipe sensors, respectively, can be

explained on the basis of the presence of both direct and indirect combustion noise. They argue that the first method

educes only direct combustion noise, whereas the second one detects the total (direct + indirect) combustion noise.

Their argument is incorrect, however. First, the inherent bias error in the coherent-output-power method can without

NASA/TM—2009-215834 6
doubt be expected to be much larger when a combustor internal sensor is utilized compared to when a core tail-pipe

sensor is used. This, by itself, is sufficient to explain the difference in the results. Second, Miles31, 33 has carried out

a coherence analysis with adjustable time delay in order to maximize the coherence and minimize the phase-angle

variation with frequency using the same engine data as the present study. He observed that the signal in the 0–200 Hz

frequency band took longer time than the signal in the 200-400 Hz range to travel to the far-field microphone. He

concluded that the coherent signal in the 0–200 Hz frequency band is partially due to indirect combustion noise and

that in the 200–400 Hz band it is due mostly to direct combustion noise. This is the opposite situation to that of the

argument in Ref. 32, where it is assumed that direct noise is dominant for frequencies less than 100 Hz and entropy

(indirect) noise peaks at 200 Hz and is dominant at higher frequencies.

frequency, Hz

SPL, dB

101102103

ANOPP total

ANOPP comb

Gzz

NOP

2s-cip1

2s-t551

3s

10 dB

frequency, Hz

SPL, dB

101102103

ANOPP total

ANOPP comb

Gzz

NOP

2s-cip1

2s-t551

3s

10 dB

(a) (b)

frequency, Hz

SPL, dB

101102103

ANOPP total

ANOPP comb

Gzz

NOP

2s-cip1

2s-t551

3s

10 dB

frequency, Hz

SPL, dB

101102103

ANOPP total

ANOPP comb

Gzz

NOP

2s-cip1

2s-t551

3s

10 dB

(c) (d)

Figure 3. Total and combustion-noise 1/3-octave SPL versus 1/3-octave center frequency in the 130o direction; (a): 48 % corrected fan

speed (flight idle); (b): 60 % corrected fan speed (approach); (c): 71 % corrected fan speed (cutback); (d): 87 % corrected fan speed

(takeoff)

E. Combustion-Noise Narrow-Band Results

Figure 4 shows the far-field narrow-band results of the source separation procedures for the flight-idle condition of 48

percent corrected fan speed in the 130o direction. Panels (a) – (c) show the total- and combustion-noise narrow-band

(8 Hz) SPL for the ‘2s-cip1’, ‘2s-t551’, and ‘3s’ methods, respectively. The black and grey curves show the total noise

signature Gzz and the threshold value ǫ

2Gzz for the coherent output power. The blue, red, and green curves in these

panels represent the corrected engine-internal-coherent noise for the three source-separation techniques, whereas the

cyan, magenta, and orange curves show the corresponding raw results. Panel (d) shows the three-signal method phase

angle, Eq. (7). The green, orange, and black curves denote the corrected phase angle, raw phase angle, and limits,

NASA/TM—2009-215834 7
frequency, Hz

SPL, dB

0 200 400 600 800 1000 20

40

60

80

Gzz

NOP

COP

COP

frequency, Hz

SPL, dB

0 200 400 600 800 1000 20

40

60

80

Gzz

NOP

COP

COP

(a) (b)

frequency, Hz

SPL, dB

0 200 400 600 800 1000 20

40

60

80

Gzz

NOP

3u

3s

frequency, Hz

phase angle, rad

0 200 400 600 800 1000 -4

-2

0

2

4

raw phase

reset phase

upper bound

lower bound

(c) (d)

Figure 4. Narrow-band (8 Hz) far-field results in the 130o direction at 48 % corrected fan speed (flight idle). (a) - (c): total and combustionnoise narrow-band SPL versus frequency; black curves—total noise signature; gray curves—noise floor; blue and cyan curves—corrected

and uncorrected combustion noise using combustor sensor and far-field microphone; red and magenta curves—corrected and uncorrected

combustion noise using turbine-exit sensor and far-field microphone; green and orange curves—corrected and uncorrected combustion

noise using combustor and turbine-exit sensors and far-field microphone. (d): three-signal method phase; black curves—limits; green and

orange curves—reset and raw phase.

respectively. Whenever the raw angle exceeds the limits, the computed raw estimate of the coherent noise is rejected

and replaced by the corresponding noise-limit value [gray curve in Panel (c)]. To simplify the comparison of the

methods, the corrected engine-internal coherent results are replotted in Figure 5(a). The black and gray curves denote

the total noise signature and the noise floor of the methods. The blue, red, and green curves show the coherent noise

estimated by the ’2s-cip1’, ‘2s-t551’, and ‘3s’ methods, respectively. The figure shows that broadband combustion

noise is identified below about 350 Hz by all three methods. It can also be seen that bias error in the ‘2s-cip1’ method,

as can be expected, leads to an underprediction of the peak value. The results from the ‘2s-t551’ and ‘3s’ methods

are in good agreement in this frequency range, however. Broadband noise in the 350–450 Hz range is only detected

by the two-signal methods. The reason that the three-signal method fails to detect broadband noise is likely due

to the lower levels of coherence in this latter frequency range. The three-signal method becomes less robust as the

coherence decreases. The red curve in this figure shows that the ‘2s-t551’ method also indicates the presence of

coherent broadband noise for frequencies larger than about 450 Hz. Even though the exact source of this noise is not

clear, it is definitely associated with the turbine.

Figure 5(b) shows the corresponding deliberately unaligned results. The color legend is the same as in Fig. 5(a).

The far-field microphone time series is here shifted by 16500 points, which corresponds to a 251.8 ms time delay, to

purposely decorrelate any broadband component present in the signals. All three methods detect what is believed to

NASA/TM—2009-215834 8
be an N2 compressor-disk tone at about 350 Hz. The origin of the hump below about 50 Hz is not clear. The nature of

the discrete peak, in both panels, at about 700 Hz is also not obvious.

Figure 6 shows the far-field narrow-band results of the three methods for the approach condition of 60 percent

corrected fan speed in the 130o direction. Panels (a) – (c) show the total- and combustion-noise narrow-band (8 Hz)

SPL for the ‘2s-cip1’, ‘2s-t551’, and ‘3s’ methods, respectively. The black and grey curves again show the total noise

signature and the threshold value for the coherent output power. The blue, red, and green curves in these panels

represent the corrected coherent noise for the three source-separation techniques, whereas the cyan, magenta, and

orange curves show the corresponding unaligned results. The figure indicates the presence of broadband combustion

noise in a frequency range up to about 400 Hz. The ‘2s-cip1’ method also here underpredicts the peak value due to the

positive-bias error inherent in the coherent-output method. The estimated combustion noise using the ‘2s-t551’ and

‘3s’ methods generally agree also here, except that a more rapid right-side fall-off of the broadband peak is predicted

by the latter method.

frequency, Hz

SPL, dB

0 200 400 600 800 1000 20

40

60

80

Gzz

NOP

2s-CIP1

2s-T551

3s

frequency, Hz

SPL, dB

0 200 400 600 800 1000 20

40

60

80

Gzz

NOP

2s-CIP1

2s-T551

3s

(a) (b)

Figure 5. Aligned (a) and unaligned (b) narrow-band (8 Hz) far-field SPL versus frequency in the 130o direction at 48 % corrected fan

speed (flight idle); black and grey curves—total noise signature and noise floor; blue. red, and green curves—corrected engine-internal

coherent noise using combustor sensor, turbine-exit sensor, and combustor and turbine-exit sensors all in combination with the far-field

130o microphone

The results in Fig. 6 are in general agreement with the ones presented by Mendoza et al.25 in their Fig. 12(b).

For the two coherent-output (two-signal) methods, there is good agreement between the present results and theirs for

both the frequency range and levels of the estimated broadband combustion noise. There are some differences for

the three-signal method results, however. The present ones are obtained using one combustor sensor, a turbine-exit

sensor and the 130ofar-field microphone. Their results were obtained using three far-field microphones (110o, 130o,

and 160o). There are significant differences for frequencies less than about 50 Hz and larger than, say, 350 Hz, with

their results suggesting the presence coherent noise, in contradiction to our results (all three methods) and their own

coherent-output results. There is reasonable agreement, however, for frequencies in the 50–350 Hz range between

their three-signal result and the present one.

IV. Summary

Within the limitations of static-engine measurements, the ANOPP total-noise predictions agree well with the EVNERT data, considering their semi-empirical base. The source-separation methods applied here to the EVNERT data

show that the ANOPP combustion-noise predictions are not adequate for the particular engine used in the test. To enable future ‘quiet’ aircraft engines, progress must be made in the understanding and modeling of the now subdominant

combustion noise and its turbine transmission/interaction.

A new and additional discriminator for the three-signal method, based on its phase angle and a statistical condition,

is also described and successfully applied herein.

NASA/TM—2009-215834 9
frequency, Hz

SPL, dB

0 200 400 600 800 1000

40

60

80

Gzz

NOP

COP-u

COP-a

frequency, Hz

SPL, dB

0 200 400 600 800 1000

40

60

80

Gzz

NOP

COP-u

COP-a

(a) (b)

frequency, Hz

SPL, dB

0 200 400 600 800 1000

40

60

80

Gzz

NOP

3s-u

3s-a

(c)

Figure 6. Far-field narrow-band (8 Hz) total and combustion-noise SPL versus frequency in the 130o direction at 60 % corrected fan speed

(approach); black and gray curves—total noise signature and noise floor. (a): two-signal method using combustor sensor and far-field

microphone; blue and cyan curves—aligned- and unaligned-case coherent noise. (b): two-signal method using turbine-exit sensor and farfield microphone; red and magenta curves—aligned- and unaligned-case coherent noise. (c): three-signal method using using combustor

and turbine-exit sensors and far-field microphone; green and orange curves—aligned- and unaligned-case coherent noise

References

1

J. F. Groeneweg, T. G. Sofrin, E. J. Rice, and P. R. Gliebe. Turbomachinery noise. In H. H. Hubbard, editor, Aeroacoustics of Fligh Vehicles:

Theory and Practice, volume 1, chapter 3, pages 151–209. NASA Reference Publication 1258, WRDC Technical Report 90-3052, 1991.

2R. J. Mahan and A. Karchmer. Combustion and core noise. In H. H. Hubbard, editor, Aeroacoustics of Fligh Vehicles: Theory and Practice,

volume 1, chapter 9, pages 483–517. NASA Reference Publication 1258, WRDC Technical Report 90-3052, 1991.

3H. H. Hubbard, editor. Aeroacoustics of Fligh Vehicles: Theory and Practice, Volume 1 and 2. NASA Reference Publication 1258, WRDC

Technical Report 90-3052, 1991.

4

J. S. Bendat and A. G. Piersol. Engineering Applications of Correlation and Spectral Analysis. Wiley-Interscience, 1980.

NASA/TM—2009-215834 10

5

A.M. Karchmer and M. Reshotko. Core noise source diagnostics on a turbofan engine using correlation and coherence techniques. 

Technical Report NASA TM-X-73535, NASA, 1976. 
6M. Reshotko, A. M. Karchmer, P. F. Penko, and J. G. McArdle. Core noise measurements on a YF-102 turbofan engine. J. Aircraft, vol. 14,

no. 7, pp. 611–612, 1977.

7A. M. Karchmer. Identification and measurement of combustion noise from a turbofan engine using correlation and coherence techniques.

Technical Report NASA TM-73747, NASA, 1977.

8A. M. Karchmer, M. Reshotko, and F. J. Montegani. Measurement of far-field combustion noise from a turbofan engine using coherence

functions. Technical Report AIAA Paper 1977-1277 (NASA TM-73748), 4th AIAA Aeroacoustics Conference, Atlanta, Georgia, 1977.

9M. Reshotko and A. M. Karchmer. Core noise measurements from a small, general aviation turbofan engine. Technical Report NASA

TM-81610, NASA, 1980.

10V. L. Doyle and M. T. Moore. Core noise investigation of the CF6-50 turbofan engine. Technical Report NASA CR-159749, NASA, 1980.

11E. A. Krejsa. New technique for the direct measurement of core noise from aircraft engines. Technical Report AIAA Paper 1981-1587

(NASA TM-82634), 17th AIAA/SAE/ASME Joint Propulsion Conference, Colorado Springs, Colorado, 1981.

12E. A. Krejsa. Application of 3-signal coherence to core noise transmission. Technical Report AIAA Paper 1983-0759 (NASA TM-83333),

8th AIAA Aeroacoustics Conference, Atlanta, Georgia, 1983.

13E. A. Krejsa. Combustion noise from gas turbine aircraft engines measurement of far-field levels. Technical Report NASA TM-88971,

NASA, 1987.

14A. M. Karchmer. Acoustic modal analysis of a full scale annular combustor. Technical Report AIAA Paper 1983-0760 (NASA TM-83334),

8th AIAA Aeroacoustics Conference, Atlanta, Georgia, 1983.

15J. H. Miles and E. A. Krejsa. Pressure transfer function of a JT15D nozzle due to acoustic and convected entropy fluctuations. J. Acoustical

Society of America, vol. 72, no. 6, pp. 2008–2019, 1982.

16E. A. Krejsa and A. M. Karchmer. Acoustic modal analysis of the pressure field in the tailpipe of a turbofan engine. Technical Report NASA

TM-83387, NASA, 1983.

17J. H. Miles, C. A. Wasserbauer, and E. A. Krejsa. Cross spectra between temperature and pressure in a constant area duct downstream of a

combustor. Technical Report AIAA Paper 1983-0762 (NASA TM-83351), 8th AIAA Aeroacoustics Conference, Atlanta, Georgia, 1983.

18J. Y. Chung. Rejection of flow noise using a coherence function method. J. Acoustical Society of America, vol. 62, no. 2, pp. 388–395, 1977.

19S. P. Parthasarathy, R. F. Cuffel, and P. F. Massier. Separation of core noise and jet noise. AIAA J., vol. 18, no. 3, pp. 256–261, 1980.

20B. J. Tester and M. J. Fisher. Engine noise source breakdown: Theory, simulation and results. Technical Report AIAA Paper 1981-2040,

7th AIAA Aeroacoustics Conference, Palo Alto, California, 1981.

21B. N. Shivashankara. High bypass ratio engine noise component separation by coherence technique. J. Aircraft, vol. 20, no. 3, pp. 236–242,

1983.

22U. von Glahn and E. A. Krejsa. Correlation of core noise obtained by three-signal coherence techniques. Technical Report NASA TM-83012,

NASA, 1982.

23W. E. Zorumski. Aircraft noise prediction program theoretical manual, part 1. Technical Report NASA TM-83199-Pt-1, NASA, 1982.

24W. E. Zorumski. Aircraft noise prediction program theoretical manual, part 2. Technical Report NASA TM-83199-Pt-2, NASA, 1982.

25J. M. Mendoza, D. K. Nance, and K. K. Ahuja. Source separation from multiple microphone measurements in the far field of a full scale

aero engine. Technical Report AIAA Paper 2008-2809, 14th AIAA/CEAS Aeroacoustic Conference, Vancouver, British Columbia, 2008.

26D. S. Weir, editor. Engine validation of noise and emission reduction technology phase I. Technical Report NASA CR-2008-215225, NASA,

2008. Honeywell Report No. 21-13843, Honeywell Aerospace, Phoenix, Arizona.

27J. S. Hsu and K. K. Ahuja. A coherence-based technique to separate internal mixing noise from farfield measurements. Technical Report

AIAA Paper 1998-2296, 4th AIAA/CEAS Aeroacoustic Conference, Toulouse, France, 1998.

28D. R. Brillinger. Time Series Data Analysis and Theory. Holden-Day, 1981.

29J. H. Miles. Aligned and unaligned coherence: A new diagnostic tool. Technical Report AIAA Paper 2006-0010 (NASA TM-2006-214112),

44th Aerospace Sciences Meeting, Reno, Nevada, 2006.

30P. D. Welch. The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified

periodograms. IEEE Transactions on Audio and Electroacoustics, vol. AU-15, no. 2, pp. 70–73, 1967.

31J. H. Miles. Spectral separation of the turbofan engine coherent combustion noise component. Technical Report AIAA Paper 2008-0050

(NASA TM-2008-215157), 46th Aerospace Sciences Meeting, Reno, Nevada, 2008.

32M. Harper-Bourne, A. Moore, and H. Siller. A study of large aero-engine combustor noise. Technical Report AIAA Paper 2008-2942, 14th

AIAA/CEAS Aeroacoustic Conference, Vancouver, British Columbia, 2008.

33J. H. Miles. Time delay analysis of turbofan engine direct and indirect combustion noise sources. J. Propulsion and Power, vol. 25, no. 1,

pp. 218–227, 2009.

NASA/TM—2009-215834 11
REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188 

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the 

data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this 

burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. 

Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB 

control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 

1. REPORT DATE (DD-MM-YYYY)

01-11-2009 

2. REPORT TYPE

Technical Memorandum 

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Noise-Source Separation Using Internal and Far-Field Sensors for a Full-Scale Turbofan 

Engine 

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Hultgren, Lennart, S.; Miles, Jeffrey, H. 

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

WBS 561581.02.08.03.18.02 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

John H. Glenn Research Center at Lewis Field 

Cleveland, Ohio 44135-3191 

8. PERFORMING ORGANIZATION

 REPORT NUMBER

E-17096 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

Washington, DC 20546-0001 

10. SPONSORING/MONITOR'S

 ACRONYM(S)

NASA; AIAA 

11. SPONSORING/MONITORING

 REPORT NUMBER

NASA/TM-2009-215834; AIAA-2009-

3220

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified-Unlimited 

Subject Category: 71 

Available electronically at http://gltrs.grc.nasa.gov 

This publication is available from the NASA Center for AeroSpace Information, 443-757-5802

13. SUPPLEMENTARY NOTES

14. ABSTRACT

Noise-source separation techniques for the extraction of the sub-dominant combustion noise from the total noise signatures obtained in 

static-engine tests are described. Three methods are applied to data from a static, full-scale engine test. Both 1/3-octave and narrow-band 

results are discussed. The results are used to assess the combustion-noise prediction capability of the Aircraft Noise Prediction Program 

(ANOPP). A new additional phase-angle-based discriminator for the three-signal method is also introduced. 

15. SUBJECT TERMS

Turbomachinery core noise; Combustion noise; Noise-source separation 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

 ABSTRACT

UU 

18. NUMBER

 OF 

 PAGES

17 

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email:help@sti.nasa.gov) 

a. REPORT

U 

b. ABSTRACT

U 

c. THIS 

PAGE

U 

19b. TELEPHONE NUMBER (include area code)

443-757-5802 

Standard Form 298 (Rev. 8-98)

Prescribed by ANSI Std. Z39-18