Clifford A. Brown, Brenda S. Henderson, and James E. Bridges

Glenn Research Center, Cleveland, Ohio

Data Quality Assurance for Supersonic

Jet Noise Measurements

NASA/TM—2010-216767

October 2010

GT2010–22545
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Clifford A. Brown, Brenda S. Henderson, and James E. Bridges

Glenn Research Center, Cleveland, Ohio

Data Quality Assurance for Supersonic

Jet Noise Measurements

NASA/TM—2010-216767

October 2010

GT2010–22545

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

Turbo Expo 2010

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Glasgow, Scotland, United Kingdom, June 14–18, 2010
Acknowledgments

This work was supported by the NASA Fundamental Aeronautics Program, Supersonics Project.

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Trade names and trademarks are used in this report for identifi cation 

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either expressed or implied, by the National Aeronautics and 

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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. 
NASA/TM—2010-216767 1 

Data Quality Assurance for Supersonic Jet Noise Measurements 

Clifford A. Brown, Brenda S. Henderson, and James E. Bridges 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

The noise created by a supersonic aircraft is a primary 

concern in the design of future high-speed planes. The jet 

noise reduction technologies required on these aircraft will be 

developed using scale-models mounted to experimental jet 

rigs designed to simulate the exhaust gases from a full-scale 

jet engine. The jet noise data collected in these experiments 

must accurately predict the noise levels produced by the fullscale hardware in order to be a useful development tool. A 

methodology has been adopted at the NASA Glenn Research 

Center’s Aero-Acoustic Propulsion Laboratory to insure the 

quality of the supersonic jet noise data acquired from the 

facility’s High Flow Jet Exit Rig so that it can be used to 

develop future nozzle technologies that reduce supersonic jet 

noise. The methodology relies on mitigating extraneous noise 

sources, examining the impact of measurement location on the 

acoustic results, and investigating the facility independence of 

the measurements. The methodology is documented here as a 

basis for validating future improvements and its limitations are 

noted so that they do not affect the data analysis. Maintaining 

a high quality jet noise laboratory is an ongoing process. By 

carefully examining the data produced and continually 

following this methodology, data quality can be maintained 

and improved over time. 

Nomenclature 

AAPL Aero-Acoustic Propulsion Laboratory 

Dj jet diameter 

HFJER High-Flow Jet Exit Rig 

Mfj flight stream Mach number 

NPRc nozzle pressure ratio, core stream 

NPRf nozzle pressure ratio, fan stream 

NTRc nozzle temperature ratio, core stream 

NTRf nozzle temperature ratio, fan stream 

RFM reticulated foam metal 

Uj jet exit velocity 

Urfm velocity at downstream face of RFM 

Introduction 

The noise created by a supersonic aircraft is a primary 

concern in the development of future high-speed planes. While 

the sonic boom is the biggest noise problem during cruise, the 

engine remains the largest source of noise at takeoff and 

landing. Historically, the most significant engine noise 

reductions on subsonic aircraft have been the result of 

increasing the bypass ratio (BPR), which also improves fuel 

efficiency. As the BPR increases, the overall jet velocity and 

the generated jet noise decrease. Unlike their subsonic 

counterparts, however, the high thrust and low drag 

requirements of supersonic flight limit the bypass ratio on a 

supersonic aircraft. Reducing the engine noise created by a 

supersonic aircraft will require developing new and existing 

technologies that mitigate the noise sources in the jet or shield 

the jet noise from the observer. These technologies will 

typically be tested and verified using high-quality scale-model 

experiments. 

Technology for a complex system, such as a jet engine, 

typically starts life in a simplified form and is tested in a 

numerical simulation or a small-scale experiment. The 

concepts that show promise advance to ever more realistic 

model-scale experiments before finally being deployed on an 

actual product. Under this paradigm, it is critical that the 

model-scale experiment appropriately represents the full-scale 

system. A methodology has been adopted at the NASA Glenn 

Research Center’s Aero-Acoustic Propulsion Laboratory 

(AAPL) to insure the quality of the supersonic jet noise data 

acquired in scale-model experiments using the facilitiy’s High 

Flow Jet Exit Rig (HFJER). This methodology has been 

developed to provide confidence that the model-scale results 

accurately reflect the acoustic performance of a noise 

reduction technology on an actual aircraft engine. 

There are many potential sources of contamination, error or 

uncertainty in a jet noise measurement. High ambient noise 

levels, control of the jet exit condition, and data acquisition 

and processing are just a few common examples. It is 

important to look at each source and evaluate its potential 

impact for a given flight regime. Subsonic jets, for example, 

produce lower levels of jet noise than supersonic jets and, 

therefore, are more susceptible to corruption by background 

noise. Conversely, the high noise levels produced by a 

supersonic jet open the potential for the non-linear 

propagation of sound, a real effect that is difficult to account 

for and, therefore, can introduce uncertainty into the 

measurement when limitations in the data processing 

capabilities are considered. Both subsonic and supersonic jet 

noise measurements may be contaminated by noise originating 

from within the jet rig, which can increase in amplitude as the 

flow rate and pressure in the rig increase. 

NASA’s Fundamental Aeronautics Program, Supersonics 

Project is tasked with developing the fundamental 

technologies for the future of supersonic flight. Maintaining 

jet rig facilities capable of producing quality jet noise 
NASA/TM—2010-216767 2 

measurements is a critical to developing these technologies. 

Previous work at NASA GRC has systematically focused on 

insuring the acoustic data quality measured from a singlestream subsonic jet (Refs. 1 and 2). A similar systematic 

approach is now used to improve the acoustic data quality 

measured from a dual stream supersonic jet with and without a 

flight stream. Problems common to all jet noise 

measurements, such as rig noise and data repeatability, are 

considered under this approach, while issues particular to 

supersonic jet noise, such as broadband shock associated noise 

and non-linear propagation effects, are given special 

consideration. 

Methodology 

A systematic approach to improving the quality of 

supersonic jet noise data has been applied using data acquired 

from the HFJER. The methodology starts by defining three 

primary sources of noise in a jet noise experiment: jet noise, 

background noise, and rig noise. Jet noise, which may be subdivided into components such as mixing noise and broadband 

shock associated noise, is generated by the flow features in the 

high-speed jet plume. Background noise encompasses all 

noise sources present in the test cell when the jet is not 

running, including any tunnel or free-jet flow used to simulate 

the effect of forward flight. All remaining noise sources are 

characterized as rig noise and are associated with the operation 

of the HFJER. 

Efforts to improve jet noise must start by identifying and 

mitigating any background noise source which has sufficient 

amplitude to impact the jet noise data. The background noise 

levels are measured before (and often after) the jet noise data 

are recorded. The background noise and jet noise may then be 

compared, on a frequency by frequency basis, to locate 

possible background noise contamination in the jet noise data. 

If the background noise is within 3 dB of the jet noise at any 

frequency, the data at this frequency are considered tainted 

and removed from the jet noise spectrum. Depending on its 

source and location, it may or may not be possible to reduce a 

background noise source that is consistently problematic. For 

example, the installation of a sound absorbing lining and sand 

bagging of access doors has reduced the background noise 

associated with the free jet that surrounds the HFJER. 

However, reducing the noise generated by the electrical 

substation located near the AAPL has been deemed 

impractical. 

After the background noise is identified, noise originating 

inside the jet rig is addressed. Common sources of rig noise, 

such as flow noise radiating through a pipe or noise caused by 

flow through a choke plate, typically increase as the jet noise 

increases making rig noise difficult to isolate. It is possible, 

however, to use the scaling properties of the jet noise itself to 

isolate the rig noise. 

Jet mixing noise is created when the high-speed jet exhaust 

leaves the nozzle and mixes with the ambient air surrounding 

the jet. The level of mixing noise produced depends on the 

strength of the shear layer between the jet and the surrounding 

air, which depends on the velocity difference between the two 

regions. Experimental and theoretical research has established 

scaling laws that show how the jet noise changes as this 

velocity difference changes. Increasing the velocity of the 

ambient air surrounding the jet while holding the conditions 

inside the jet rig constant reduces the velocity difference and, 

therefore, the jet noise. The jet noise reductions can be 

predicted by the jet noise scaling laws. Rig noise, which is 

dictated by the flow conditions inside the rig, will remain 

constant even as the jet mixing noise is reduced, exposing 

problematic rig noise in the measured spectrum. 

Once the background and rig noise sources have been 

identified, the focus can shift to the jet noise data itself in 

order to further define the uncertainty in the measurement. 

There are two important parts to this process. First, the jet 

control systems used to set and maintain the stated jet exit 

condition are a potential source of uncertainty. Each 

temperature or pressure sensor used to determine the jet 

condition has an uncertainty associated with it. Fluctuations in 

the supply air pressure and temperature, and the corresponding 

corrections applied at the control valves, add to the jet 

condition uncertainty. Second, the instrumentation and data 

system used to acquire the noise data will introduce some 

uncertainty into the measurement. Knowledge of the 

uncertainty in the measurement will lend validity to the 

measurement and assist in the interpretation of the results. 

The jet noise data collected in an experiment should be 

independent of the jet rig used for the test. In some cases 

comparing data acquired using two different jet rigs can be 

used to identify rig noise at one or both facilities. While it is 

unlikely that the sources of rig noise are the same, both jet rigs 

have some internal noise, it is unlikely the sources of this 

noise behave in the same way due differences inherent in the 

individual jet rigs. This type of comparison test is rarely done 

because the same model hardware must be tested at the same 

jet exit conditions on both jet rigs. This is often difficult due to 

size differences between the jet rigs, particularly at the model 

mount point, or because of pressure, temperature, or mass 

flow limitations at one of the two rigs. Where possible, 

however, these comparison tests can give a good indication of 

the jet rig independence of data collected at both facilities. 

The proposed methodology for improving the quality of a 

jet noise measurement is a multi-step process. Similar methods 

have been used at NASA GRC to validate the quality of 

acoustic data from a single stream jet rig (Refs. 1 and 2) and 

from a fan model in a wind tunnel (Ref. 3). First, extraneous 

noise sources are identified and, if possible, removed from the 

data, leaving only the jet noise. Then, uncertainty in the jet 

noise measurement is quantified, establishing an estimate of 

the accuracy of the measurement. Finally, the measurement 

precision is studied, determining the repeatability of the 

measurement. Repeatability is often more important than 

absolute accuracy in scale-model experiments. If carefully 
NASA/TM—2010-216767 3 

followed, these procedures should give added validity to the 

jet noise data produced from any jet rig facility. 

Test Setup 

The HFJER, located in the AAPL at GRC (Fig. 1), was 

used for these experiments. The HFJER is a dual flow jet rig 

capable of operating in both internally mixed and separate 

flow nozzle configurations. Air is supplied to the rig by a 

remote compressor. Jet exit temperatures up to 1425 °F, on the 

core stream only, can be obtained using a natural gas 

combustor (fan stream temperature is limited to below 

250 °F). The maximum flow rate through the jet rig is 

20 lbm/s. The HFJER is located inside the Nozzle Acoustic 

Test Rig (NATR), a 53 in. diameter freejet that provides a 

simulated flight stream at speeds up to Mach 0.3. The AAPL, 

a geodesic dome with a radius of 65 ft, is lined with sound 

absorbing wedges to create an anechoic environment at 

frequencies above 200 Hz. More detail about the AAPL can be 

found in Reference 4. 

A separate flow nozzle system, representative of an exhaust 

system that might be on a small commercial supersonic 

aircraft, was used for this test. The system, shown in Figure 2 

has a bypass ratio (BPR) of 5 and includes a pylon. The angle 

of the pylon relative to the sideline microphone array is 

approximately 122°. The pylon angle relative to the overhead 

array is approximately 190° (Fig. 3). 

Figure 1.—The AAPL located at GRC houses the HFJER, right 

center. The blue track across the dome holds the overhead 

arc microphone array. 

Figure 2.—The separate flow, BPR 5 nozzle system used for 

the supersonic jet noise quality assessment tests mounted 

on the HFJER at the AAPL. 

Figure 3.—Schematic drawing of the jet, pylon, and 

microphone array layout for these tests. The angle of the 

pylon relative to the sideline microphone array is 

approximately 122° and the pylon angle relative to the 

overhead arc microphone array is approximately 190°. Note 

that this drawing is not to scale. 

Acoustic data were acquired during this test using two 

separate microphone arrays. The first array, referred to as the 

overhead arc array, was mounted above the jet, at 22.5° 

relative to the top of the model, on a 45-ft constant radius arc 

covering polar angles from approximately 45° to 160° (Fig. 3). 

This array uses 24 microphones, placed at 5° increments, and 

is the standard far-field acoustic array for the HFJER rig. A 

second microphone array, referred to as the sideline array, was 
NASA/TM—2010-216767 4 

added for these tests. It was placed at the same location 

relative to the jet as the array at the NASA Langley Research 

Center’s Jet Noise Laboratory (JNL). The sideline array uses 

28 microphones and covers polar angles from 45° to 150°. 

Bruel & Kjaer type 4939 1/4 in. microphones were deployed 

in both arrays. 

Data from the two microphone arrays were acquired 

simultaneously and digitized, at a 200 kHz sample rate, using 

a DataMAX Instrumentation Recorder from R.C. Electornics. 

The raw data were filtered with a 90 kHz low pass filter. Once 

acquired, data were converted from volts to Pascals, using a 

calibration value measured for each microphone, and 

transformed to narrowband spectra using a standard FFT 

routine with a 214-point Kaiser window giving a spectral bin 

width of 12.21 Hz. Background noise was then subtracted 

from the measured data at each frequency in the spectrum. 

Next, the data were corrected for the spectral response of the 

microphone using the calibration supplied for each 

microphone by Bruel & Kjaer. The data were also corrected 

for sound refraction caused by the free jet shear layer. Finally, 

the data were transformed to a lossless condition and scaled to 

a measurement distance of 1 ft by correcting for atmospheric 

attenuation and spherical spreading. 

Ambient temperature, pressure, and humidity and all jet 

conditions are recorded by the facility computer system 

(ESCORT). Each jet exit condition to be tested is assigned a 

“setpoint” code and input into the ESCORT computer. Each 

setpoint is defined by a core nozzle pressure ratio (NPRc), a 

fan nozzle pressure ratio (NPRf), a core nozzle temperature 

ratio (NTRc), a fan nozzle temperature ratio, and a flight 

stream Mach number (Mfj). The nozzle pressure ratios are 

defined using the total pressure in the jet rig upstream of the 

nozzle and the ambient pressure in the AAPL (NPR = 

Pt,j/Pamb); the nozzle temperature ratios are defined as the jet 

total temperature relative to the ambient temperature (NTR = 

Tt,j/Tamb). Using this information, ESCORT then provides a 

real time total error value, updated once per second, which 

reflects the difference between the actual operating condition 

and the desired setpoint. The total error value must remain 

below 0.5 percent during the entire record time for the data to 

be accepted, setting an upper bound on the measurement error 

due to the actual jet exit condition. A table showing all the jet 

conditions tested is shown in the Appendix. Note that the first 

four digits in the setpoint number indicate the jet condition 

and the last digit indicates the flight stream Mach number as 

described in the Appendix. 

Separation of Noise Sources 

Any acoustic measurement involving many active systems 

will include noise from multiple sources. A methodology has 

been developed to account for the extraneous noise sources 

that may contaminate the measurements when acquiring jet 

noise data. The two extraneous noise sources defined in this 

methodology are background noise and rig noise. 

Background Noise 

The first source of noise identified was background noise. 

Background noise, which includes noise created by the 

simulated flight stream, is typically more of an issue in 

subsonic jet noise measurements. Supersonic jets, which 

produce more jet noise than their subsonic counterparts, 

should generate jet noise levels well above the background 

noise level. If background noise is a problem in a supersonic 

jet noise test, it would first appear at the jet condition with the 

lowest jet velocity and the highest flight stream Mach number 

(setpoint 50504, Mfj = 0.35). At this setpoint, the jet noise is at 

the lowest level of any point tested while the background 

noise is the highest level likely to occur during testing because 

of the high-speed flight stream. Figure 4 shows the 

background noise level, with a Mfj = 0.35 flight stream, 

relative to the four lowest jet noise conditions tested. As 

expected, the jet noise is well above the background noise at 

all setpoints and most frequencies. It is only at the lowest 

frequencies, below approximately 300 Hz (the anechoic limit 

of the AAPL is around 200 Hz), that the background noise 

level comes to within the defined 3 dB threshold which 

indicates background noise contamination. The standard 

acoustic data processing will remove the contaminated jet 

noise data in this low frequency region and, therefore, 

additional steps are not required to address the background 

noise. 

Rig Noise 

Once the background noise has been addressed, the rig 

noise, and its impact on the measured spectrum, must be 

determined. In this approach, rig noise is broadly defined as 

any noise that is not background or jet noise. Typically, the 

compressor, combustor, control valves and the support 

Figure 4.—Background noise measured with a Mfj = 

0.35 flight stream and compared to the lowest noise 

jet conditions tested. 

Frequency (Hz)

PSD (dB)

102 103 104 10 0 5

20

40

60

80

SP50504

SP50514

SP50524

SP50534

Background - Mfj=0.35
NASA/TM—2010-216767 5 

Figure 5.—Schematic showing the structure of a single stream 

supersonic jet. 

structure used to supply high pressure, high temperature air to 

the test nozzle are the largest sources of rig noise. Since the air 

flow through the rig is directly related to the jet condition, the 

rig noise will generally change as the jet exit condition 

changes. Separating the rig noise from the jet noise, therefore, 

requires a basic understanding of the physics behind the jet 

noise sources. 

There are two primary noise sources in a supersonic jet: 

broadband shock associated noise and jet mixing noise. 

Broadband shock associated noise (BBSN) is created by the 

interaction between the turbulence in the mixing shear layer 

and the shock cells in the jet plume (Fig. 5). The peak 

frequency and amplitude of the BBSN depend on the shape 

and axial location of the shock cells, which are dictated by the 

nozzle pressure ratio. While it is important not to confuse 

BBSN with rig noise, the direct connection between the BBSN 

and the nozzle pressure ratio makes it difficult to identify any 

rig noise (which also changes with NPR) by only changing 

characteristics of the BBSN. The jet mixing noise, however, 

can be used to determine what impact the rig noise has on the 

measured spectrum. 

The free-shear boundary layer that forms between a highspeed jet and the surrounding fluid is naturally unstable 

(Fig. 5). As the fluids mix in the jet plume, Kelvin-Helmholtz 

instability waves are formed. These instability waves grow 

and decay as the jet develops, governing the formation and 

destruction of the unsteady turbulent flow structures. These 

structures are responsible for the creation of the jet mixing 

noise that characterizes the broadband nature of jet noise. 

These structures can range in size from the large-scale eddies, 

on the order of the jet diameter, that transport energy through 

the jet plume to the small, microscopic eddies that dissipate 

energy through viscous forces. Experimental and theoretical 

research has shown that the jet mixing noise may be predicted 

using the velocity difference between the jet and the 

surrounding air (Ref. 5). Thus, there are two ways to change 

the amplitude of the jet mixing noise in an experiment: change 

the jet exit velocity or change the flight stream velocity. This 

basic understanding of the physics behind the jet mixing noise 

source can be used to search for rig noise in a jet noise 

measurement. 

Figure 6.—Separating the rig noise from the jet noise, for 

setpoints 50500, 50501, and 50504 at the 90° measurement 

location, by using three different flight velocities. The circled 

data shows where the jet mixing noise decreases with the 

increasing flight stream velocity to reveal the internal rig 

noise. 

A jet rig consists of many pipes, valves, and flow 

conditioners that are all potential sources of rig noise. 

Additionally, the characteristics (i.e., amplitude and 

frequency) of these potential rig noise sources will change as 

the flow parameters (e.g., pressure, velocity) inside the rig 

change. Changes to these flow parameters will also change the 

jet exit condition and, therefore, the jet noise making it 

difficult to isolate the rig noise from the jet noise. However, if 

the jet noise changes only because the flight stream velocity 

changes, then the jet exit condition, internal flow parameters 

and rig noise will all remain the same. Such a change allows 

the rig noise to be separated from the jet noise. 

Data were acquired at setpoint 50500 (NPRc = 1.600, NPRf

= 2.000) with no flight stream (Mfj = 0.0), a Mach 0.1 flight 

stream (Mfj = 0.1), and a Mach 0.35 flight stream (Mfj = 0.35) 

to demonstrate how the jet noise and rig noise sources may be 

separated (Fig. 6). A comparison of the spectra from these 

points shows that the measured noise levels scale with changes 

to the relative shear layer velocities through the low and midfrequency range. At higher frequencies, however, the data 

does not scale in the same way. When there is no flight stream 

(Mfj = 0.0), the spectrum decreases almost linearly as 

frequency increases. As the flight stream velocity increases, 

however, a pronounced ‘hump’ forms in the spectra indicating 

the presence of a noise source that does not vary with changes 

to the velocity difference across the shear layer in the same 

way jet noise does. This noise source, therefore, is not jet 

noise but rig noise as defined in this methodology. Note that in 

these data the rig noise and the jet noise have similar levels, so 

the resultant amplitude in the affected frequency range is a 

Frequency (Hz)

PSD (dB)

102 103 104 10 65 5

70

75

80

85

90

95

100

Mfj=0.0

Mfj=0.1

Mfj=0.35
NASA/TM—2010-216767 6 

combination of the two sources. If the flight stream velocity 

were further increased (Mfj = 0.35 is the limit at this facility) 

so that there was no velocity difference between the two 

streams, then the spectra would eventually collapse onto one 

line representing only the rig noise. 

Uncovering rig noise in a jet noise measurement and 

eliminating it from future measurements are two very different 

tasks. There are so many potential sources of noise within a jet 

rig that it can be difficult to isolate the one that is actually 

causing the problem. The data in Figure 6 show the presence 

of a noise source in the HFJER rig but little information as to 

its origin. One approach to identifying the source of this rig 

noise is to start at the nozzle exit and begin working upstream 

in search of the rig noise source. In the HFJER, moving 

upstream from the nozzle exit shows a pair of coarse mesh 

screens used for flow conditioning, followed by a section of 

reticulated foam metal (RFM), and then a series of choke 

plates. Removing the flow conditioning screens did not change 

the characteristics of the rig noise, therefore the RFM was 

investigated next. The RFM was added to the rig in part to 

muffle the noise created by the choke plates farther upstream. 

Consequently, removing the RFM from the rig would have 

taken its potential contribution to the rig noise away but it 

would also reveal choke plate noise that was not previously 

present in the spectra. Therefore, a small test stand, consisting 

of a one-inch diameter pipe (fed by a shop air source), a series 

of choke plates, and a few small disks of RFM, was built to 

determine the character of RFM and choke plate noise. The 

results of this sub-scale test showed the RFM as the primary 

source of the internal rig noise revealed in Figure 6. 

The flow inside the HFJER winds its way through the many 

small passages in a section of RFM before reaching the nozzle 

exit and produces noise in the process. Data were acquired at 

several different flow rates using a sub-scale test rig to 

determine that the noise peaks around 35 kHz and scales 

approximately as Urfm6

, where Urfm is the flow velocity at the 

downstream face of the RFM section. The RFM associated rig 

noise, therefore, will increase more slowly than the jet noise 

(which increases like Uj

8

 for subsonic and low supersonic jets) 

and will cease to be a problem for jets above a certain exit 

velocity. Using this information, the RFM related rig noise 

could be modeled to determine where and to what extent it 

affects the jet noise data. The RFM noise data were modeled 

as: 

2

peak

RFM peak 10 Rig Noise 40log 



 



   f

f A (1) 

where fpeak is the frequency where the RFM noise peaks, the 

factor –40 governs the spectral width of the noise, and the 

value of Apeak is given by: 

Apeak  60 log10   Urfm  28 (2) 

Figure 7.—The effect of rig noise on the spectra measured at 

90° relative to the jet axis for setpoints 50500, 50501, and 

50504, and including the rig noise as determined using a 

model developed from a series of sub-scale tests. 

where the factor –28 is a constant used to adjust the spectra for 

measurement distance. The modeled RFM related noise is 

shown in Figure 7 with the data acquired at setpoints 50500, 

50501, and 50504. 

The RFM related rig noise definitely has an impact on the 

spectra measured at the quietest jet noise conditions in the test 

matrix (Figs. 6 and 7)1

. Ideally, this rig noise would be 

removed from the measured data entirely, either by removing 

or muffling the rig noise source, before completing the 

remainder of the test matrix. However, this was not practical 

due to the design of the jet rig, the time constraints, and the 

goals of the immediate experiments. Therefore, understanding 

the rig noise characteristics (i.e., frequency range, amplitude, 

and spectral shape) became a critical part of quality control in 

these jet noise experiments. 

Jet Noise Data Quality 

Following the methodology for data quality assurance, the 

next step is to examine the jet noise data itself for possible 

problems. First, the experimental uncertainty in the 

measurements is quantified. This involves examining the 

measurement process from the microphone through the data 

acquisition system. Then, the choice of measurement location 

is examined. Finally, the repeatability of the jet noise data, 

both on a single jet rig and between two different jet rigs, is 

considered. Although not perfect, this systematic approach 

should give a good understanding of the jet noise data quality 

and show where improvements may be needed. 

 

1

Modifications to the HFJER to eliminate or greatly reduce the rig noise are 

underway in preparation for a future series of sub-sonic jet noise test. 

Frequency (Hz)

PSD (dB)

102 103 104 10 60 5

65

70

75

80

85

90

95

100

Mfj=0.0

Mfj=0.1

Mfj=0.35

Rig Noise
NASA/TM—2010-216767 7 

Measurement Uncertainty and Precision 

The uncertainty of a measurement is used to assign an error 

bar, or expected deviation from the true value. There are 

several sources of uncertainty in the acoustic data acquired in 

the AAPL. Starting at the microphone and going through the 

data acquisition system, analysis has shown that the primary 

sources of uncertainty (Table 1) are the microphone calibrator 

and the correction for the spectral response of each individual 

microphone (Refs. 2 and 6). A Bruel & Kjaer type 

4220 pistonphone, with a manufacturer specified uncertainty 

of 0.15 dB, is used to determine the volts-to-Pascals pressure 

conversion factor for each microphone prior to each run. The 

manufacturer also provides a correction to account for the 

spectral response of each microphone with an uncertainty of 

0.27 dB. 

Source dB, 

Low frequency 

dB, 

High frequency 

pistonphone 

(B&K 4220 spec.) 

0.15 0.15 

mic. spectral calibration 

(B&K calibration spec.) 

0.27 0.27 

jet condition 

(0.5% error in Uj) 

0.17 0.17 

1/3 octave average 

(2, 1/3 octave integration 

at 90% confidence) 

0.33 0.01 

-ORnarrowband average 

(2, 150 averages at 90% 

confidence) 

0.61 0.61 

Total – 1/3 octave SPL 0.96 0.74 

Total – narrowband PSD 1.24 1.24 

After the data acquisition system, the next significant 

source of uncertainty is the ability to define, set, and maintain 

a specific jet condition. To minimize this uncertainty, a system 

has been developed to calculate a total error based on the 

difference between the desired jet setpoint parameters (NPRc, 

NPRf, NTRc, NTRf, Mfj) and the actual values. This total error 

must remain below 0.5 percent throughout the data acquisition 

time (8 sec) for a point to be considered valid. Because jet 

noise scales with the jet velocity, the uncertainty in the noise 

measured attributed to an error in the jet condition can be 

estimated by computing the maximum and minimum jet 

velocities that could occur if the jet parameters varied by 

0.5 percent. For example, the uncertainty in the acoustic 

measurements due to a 0.5 percent variation in jet condition in 

a single steam jet is estimated to be approximately 0.17 dB. 

Finally, the data processing techniques employed add 

uncertainty to the measurement. Data at each setpoint is 

acquired for a period of 8 sec at a sample rate of 200 kHz and 

processed using 214 point windows with 50 percent overlap. 

Any window where the time signal is clipped when digitized 

is removed. A 2

 analysis considering 150 windows at 

90 percent confidence shows that the uncertainty for 

Figure 8.—Precision, or repeatability, of data acquired at 90° 

(top) and 150° (bottom) over several day and repeated 

model changes from the HFJER using a single stream 

nozzle configuration, with chevrons, at an ideally expanded 

jet Mach number of 1.47. These data are scaled to a 1 ft 

lossless condition. 

narrowband spectral data is 0.61 dB across the frequency 

spectrum. Integrating across frequencies to form 1/3 octave 

spectra reduces the uncertainty to 0.33 dB at low frequencies 

and nearly eliminates it at high frequencies. Combining these 

values yields a total measurement uncertainty of 1.24 dB for 

narrowband spectra and less than 1.0 dB for 1/3 octave 

spectra. 

After the measurement uncertainty is known, the precision 

of the measurement should be considered. Unlike the 

uncertainty, however, precision, or repeatability, is not 

calculated from manufacturer’s specifications. Instead, it 

involves the statistical analysis of repeated experiments using 

the same model hardware at the same jet conditions. Figure 8 

shows a repeat data set acquired over several days and model 

changes using a single stream nozzle, with chevrons, on the 
NASA/TM—2010-216767 8 

HFJER at an ideally expanded jet Mach number of 1.47. 

These data, which are scaled to a distance of 1 ft from the 

nozzle exit with atmospheric attenuation removed (1 ft 

lossless condition), show that the repeatability over most of 

the frequency range is approximately 0.5 dB. Only at the 

highest frequencies, where the atmospheric attenuation 

correction is the greatest, does the repeatability degrade. The 

AAPL is an outdoor facility and variations in weather 

conditions over several days may have caused some of this 

discrepancy at these high frequencies. Although this analysis 

is based on data acquired using a single stream jet, it should be 

noted that both the core and fan streams were used to supply 

the heated air to the nozzle and, therefore, the repeatability for 

a dual or separate stream nozzle configuration should be 

similar. 

Measurement Location 

The location of the microphones is an important part of 

both the test setup and data analysis. There are several factors 

to consider when choosing a measurement location. Often the 

physical constraints of the test facility limit the placement of 

the microphones to locations that are not ideal. In this case, it 

is important to understand the impact of measurement location 

on the jet noise data. 

Jet noise data are often scaled to represent a measurement at 

another distance, either to compare with data acquired in 

another facility or to transform data acquired in a laboratory to 

a flyover distance. Two assumptions are commonly used in 

these transformations. First, an assumption about the source 

location is needed to determine the correct propagation 

distance of the sound. Second, some assumption regarding the 

behavior of the sound as it propagates through the atmosphere 

is needed. 

Jet noise is produced by many sources distributed 

throughout the plume. In general, the higher frequency sources 

are located near the nozzle exit while the lower frequency 

sources are farther downstream. Ideally, the measurement 

location is sufficiently far away from the jet that this 

distributed source appears to be a point source to the 

microphone. In such a case the microphone is considered to be 

in the geometric far-field of the jet and the nozzle exit can be 

used to represent the location for all the frequencies in the jet 

noise spectrum. This simplifies the data processing because an 

accurate description of the source distribution is not required. 

If the measurement location is not in the geometric far-field of 

the jet, some error will be incurred if the nozzle exit is used to 

represent the entire noise source as the actual distance from 

the noise source to the microphone will be different than the 

assumed distance. Previous research has shown that the 

geometric far-field begins approximately 50 jet diameters (Dj) 

from a single stream subsonic jet (Ref. 1). Figure 9 shows data 

measured at several distances from a gas dynamic Mach 

(Vj/cj) 1.5 jet and scaled to a distance of 1 ft, with atmospheric 

attenuation removed, assuming all noise sources are located at 

the nozzle exit. The geometric far-field begins at the point 

Figure 9.—Spectra measured from a single stream Mach (Vj/cj) 

1.5 jet at 90° (top) and 150° (bottom) from the jet axis at 

several distances from the nozzle exit and scaled to a 

common distance of 1 ft from the nozzle exit with 

atmospheric attenuation effects removed (1 ft lossless 

condition) assuming the measurement is in the geometric 

far-field. 

beyond which the data can be scaled to any common distance 

with the same result while assuming all frequencies originate 

at the nozzle exit. These data support using 50 Dj as the 

minimum geometric far-field measurement distance for 

supersonic single stream jets which contain both mixing noise 

and broadband shock noise. 

Two microphone arrays were deployed for the HFJER rig 

validation test: a 45-ft radius arc array and an 11.5-ft sideline 

array. Data were acquired using both arrays and processed to a 

1 ft lossless condition assuming that all noise sources are 

located at the nozzle exit. The sideline array is approximately 

20 Dj from the jet at 90° and 36 Dj from the jet at 150° (where 

Dj is the fan nozzle exit diameter).
NASA/TM—2010-216767 9 

Figure 10.—Spectra showing data acquired at setpoint 50230 

using an arc array (45-ft radius) and an 11.5-ft sideline array 

at 90° (top) and 150° (bottom). All data were scaled to a 

distance of 1 ft and atmospheric attenuation effects removed 

(1 ft lossless condition) assuming all noise sources were 

located at the nozzle exit. 

The arc array is approximately 80 Dj from the jet at all angles. 

Based on the above analysis using data collected from a single 

stream jet, the arc array is in the geometric far-field of the jet 

while the sideline array is not. Therefore, spectral differences 

should be expected in the separate flow jet noise data 

measured using these arrays when scaled to a 1 ft lossless 

condition. Figure 10 shows data from both arrays acquired 

simultaneously at setpoint 50230 (NPRc = 1.600, NPRf = 

2.200, NTRc = 2.310) and scaled to a 1 ft lossless condition. 

The scaled noise level around the peak frequencies measured 

by the sideline array at 90° is 2 dB lower than that measured 

by the arc array. At higher frequencies, where the noise source 

is close to the nozzle exit, data from the two arrays agree. Data 

measured from the two arrays at the 150° location also shows 

a 2 dB difference, but at frequencies above the peak, cause by 

a shift toward higher frequencies in the scaled data from the 

sideline array. Overall, these results are consistent with the 

previous work and show how factors such as measurement 

distance should be considered in data analysis and 

presentation. 

The second assumption commonly applied when 

transforming jet noise data to another distance involves the 

propagation of sound. Normally, it is assumed that sound 

propagation is a linear process. If, however, the sound levels 

are especially high, as might be the case in a supersonic jet, 

the noise propagation can be a non-linear process, leading to 

problems associated with accurately scaling the data. 

The propagation of sound is a linear process in most cases 

and data are easily corrected for the distance traveled and the 

losses incurred due to atmospheric attenuation. As a sound 

wave travels in space away from its source, the energy spreads 

in all directions. This spherical spreading reduces the peak 

amplitude of the sound wave by spreading the same sound 

energy over a larger area. Spherical spreading over a distance 

is described for all frequencies by the equation: 















   2

2

2

1 10 dB 10log d

d (3) 

where d1 and d2 are the two distances from the source. 

Applying only this scaling equation results in the “lossless” 

propagation of sound where no additional energy is lost to the 

atmosphere. In reality, some additional energy is lost as the 

sound wave travels and continually interacts with air 

molecules and water vapor in its path. Corrections for the 

atmospheric absorption of sound energy (typically in dB/m) 

exist and are fairly reliable when the sound propagates linearly 

(Ref. 7). These corrections are based on the ambient 

temperature, pressure, and humidity. 

When the sound levels become sufficiently high, however, 

the sound propagation process is no longer linear. The waves 

steepen as they travel, transferring sound energy from lower to 

higher frequencies. The distance scaling rules for non-linear 

sound propagation depend not only on the distance traveled 

and atmospheric conditions but also on the amplitude and 

frequency content of the sound. It is important to recognize the 

impact that applying linear propagation rules to non-linearly 

propagating sound has on the scaled spectrum. 

The nonlinear transfer of energy from low frequencies to 

high appears to occur in the arc array data presented in 

Figure 11. The spectrum from the arc array abruptly increases 

in amplitude around 60 kHz while the data from the sideline 

array continues to decrease in amplitude. Viswanathan et al., 

used this method of acquiring data simultaneously at different 

distances to identify non-linear propagation in data recorded 

from supersonic jet (Ref. 8). The spectra presented in 

Figure 11 were transformed to a distance of 1 ft and corrected 

for atmospheric losses using the standard linear correction 

method. Data are shown for all of the jet conditions (both cold 

and hot) set during the test. Each spectra obtained with 

Frequency (Hz)

PSD (dB)

102 103 104 10 70 5

75

80

85

90

95

100

105

110

Sideline Array

Arc Array

90o

Frequency (Hz)

PSD (dB)

102 103 104 10 70 5

75

80

85

90

95

100

105

110

115

120

125

Sideline Array

Arc Array

150o
NASA/TM—2010-216767 10 

Figure 11.—PSD (dB) acquired using the sideline array and arc 

array, at the 150° location, for all the cold jet conditions (top) 

and all the hot jet conditions (bottom). Note that the spectra 

for each setpoint has been shifted to allow all the data to 

appear on one plot. 

the arc array shows some high frequency noise increase, 

although the effect is much smaller in the data collected at the 

cold jet conditions. In the previous work Viswanathan et al., 

found that non-linear propagation only occurred at hot jet 

conditions (Ref. 8). Additionally, the sound amplitudes 

measured in their experiments where significantly higher than 

those measured here which should lead to stronger non-linear 

sound propagation. More work is needed to confirm whether 

the increase in high frequency noise shown in Figure 11 is the 

result of non-linear propagation or some other effect. It is 

important to note, however, that non-linear propagation effects 

can impact the spectra measured from supersonic jets and, 

because of this, measurement location can affect the results 

even when the data is scaled to a common distance. 

Jet Rig Independence 

Model scale tests are designed to prove the capabilities of a 

new technology without the expense required to build and 

certify the full-scale hardware. Often, a technology is tested at 

increasingly larger scale, each time using hardware more 

representative of the final product. In the process, tests may be 

conducted on several rigs, possibly in completely different 

facilities. It is important, therefore, that the data collected be 

independent of the facility where it is acquired. 

No two jet rigs are exactly the same. Each rig has its own 

strengths and weaknesses. If the jet noise data obtained at one 

facility is different than that obtained at another, it is difficult 

to identify which is more accurate. Nevertheless, documenting 

the differences does provide insight regarding the capabilities 

of the different facilities. 

A nozzle system was recently tested at two NASA 

laboratories: the Aero-Acoustic Propulsion Laboratory at the 

GRC and the JNL at the LRC. These tests were conducted 

using the same model hardware, jet conditions, and 

measurement locations, in order to document the facility 

independence of the jet noise data. Figure 12 shows a 

comparison of the data acquired during these tests (setpoint 

50761, NPRc = 1.800, NPRf = 2.500, NTRc = 2.310, Mfj = 

0.1). A high pass filter was used when acquiring the data at 

JNL resulting the sharp drop in low frequency sound levels. 

The peak jet mixing noise, around 1 kHz, is increased slightly 

higher in the AAPL data at the 90° and 120° measurement 

locations but agrees well at the 150° location. Also, there is a 

small difference in the high frequency noise at the far 

upstream and downstream locations. The origin of this 

discrepancy is currently unknown as the AAPL rig noise is 

predicted to be well below the jet noise at this setpoint. It is 

possible these differences are within the ability to repeat the 

test at the different laboratories and additional tests are 

required to establish the cross-facility repeatability. The 

broadband shock associated noise, particularly at the 90° and 

120° locations, also shows some differences between the two 

data sets. 

BBSN originates in a supersonic jet when the turbulent 

flow structures, traveling downstream, interact with shock 

cells in the jet plume. The characteristics of BBSN, therefore, 

are highly dependent of the location and strength of the shock 

cells in the jet. In a single stream jet, the shock cell location 

and structure are driven by the nozzle pressure ratio. In a 

separate flow jet, however, the situation is more complicated. 

At setpoint 50761 (NPRc = 1.800, NPRf = 2.500, NTRc = 

2.310, Mfj = 0.1, Figure 12), the fan stream is supersonic while 

the core stream is subsonic. Tam et al. studied this scenario 

and found that there are two mechanisms for generating 

shocks in a separate flow nozzle (Ref. 9). The first shock cell 

structure originates at the fan nozzle exit and results from the 

pressure mismatch between the fan stream and the ambient 

pressure. The second, and weaker, shock cell structure 

Frequency (Hz)

102 103 104 105

Sideline Array

Arc Array

150o

SP50500

SP50530

SP50550

SP50560

SP50580

SP50600

Frequency (Hz)

102 103 104 105

Sideline Array

Arc Array

150o

SP50230

SP50250

SP50260

SP50710

SP50720

SP50730
NASA/TM—2010-216767 11 

Figure 12.—Spectra acquired at the AAPL and JNL for 

setpoint 50761 at the 60° (top, left), 90° (top, right), 

120° (bottom, left) and 150° (bottom right) 

microphone locations. Note that the AAPL sideline 

array was deployed to match the microphone 

locations, relative to the jet exit, of the JNL array so 

all the data were acquired a the same physical 

locations. 

originates at the core nozzle exit plane where the supersonic 

fan flow turns so that it runs parallel to core flow. The 

differences observed in the broadband shock associated noise 

between the JNL and AAPL data (Fig. 12) are most likely 

attributed to differences in this secondary shock cell structure 

created by the design of the respective jet rigs. While every 

effort was made to exactly replicate the test at the two 

laboratories, an adapter was required to mount the nozzle 

hardware (originally intended only for the JNL) to the HFJER. 

This adapter extended the fan stream nozzle exit downstream 

by approximately 0.176 in., reducing the distance between the 

fan and core stream exit planes. This shift creates subtle 

differences in the secondary shock cell structure formed at the 

two jet rigs which could account for the small differences in 

BBSN measured at the two facilities. 

Conclusions 

A methodology has been developed at the NASA Glenn 

Research Center’s Aero-Acoustic Propulsion Laboratory to 

assure the quality of the jet noise data measured using the 

High Flow Jet Exit Rig. Three noise sources were identified in 

the measured data: background noise, rig noise, and jet noise. 

Data were acquired to show that the impact of the background 

noise on the jet noise spectra was minimal. Additional data 

were acquired to determine the spectral characteristics of the 

rig noise. A model of the rig noise was developed to examine 

the affect that the rig noise has on the jet noise spectra. 

Attention was then focused on evaluating the measured jet 

noise. This process began by analyzing the uncertainty in the 

noise measurement from the microphone through the data 

acquisition system and including data processing techniques. 

Next, the impact of measurement location was studied by 

examining spectral differences caused by varying the distance 

between the jet and the microphone and, in a supersonic jet, 

the possibility of non-linear sound propagation. Finally, the 

facility independence of the noise measurements was 

examined using data acquired at two different NASA 

laboratories. At each step, the results were documented for 

future reference. This documentation will be used as a 

reference when analyzing future data or evaluating future 

changes to the jet rig design. 

Scale-model jet noise experiments are conducted to 

determine the noise impact of a technology while incurring 

significantly less expense than otherwise required to build and 

certify the full-scale hardware. To complete this mission, the 

data collected in these laboratory experiments must be able to 

accurately predict the noise characteristics of the full-scale jet. 

Improving data quality is an ongoing, two part process. First, 

the problems inherent in any measurement, such as rig noise 

or data system uncertainty, must be carefully documented so 

they do not impact the data analysis. Second, the problems 

identified should be minimized. By carefully examining the 

data produced and continually following this methodology, 

data quality will be maintained and even improve over time. 

References 

1. Brown, Cliff and Bridges, James, “Small Hot Jet Acoustic 

Rig Validation,” NASA/TM—2006-214234, 2006. 

2. Bridges, James and Brown, Clifford, “Validation of the 

Small Hot Jet Acoustic Rig for Jet Noise Research,” 

AIAA–2005–2846, 2005. 

3. Brown, Clifford A. and Schiffer, Nicholas A., “Low 

Frequency Noise Contamination in Fan Model Testing,” 

GT2008–50850, Proceedings of the ASME Turbo Expo 

2008, June 9–13, 2008. 

4. Bridges, James, “Measurements of Turbulent Flow Field 

in Separate Flow Nozzles with Enhanced Mixing Devices 

– Test Report,” NASA/TM—2002-211366, 2002. 

5. Viswanathan, K., “Aeroacoustics of Hot Jets,” J. Fluid 

Mech., vol. 516, pp. 39–82, 2004. 

6. Bridges, J., Khavaran, A., and Hunter, C.A., “Assessment 

of Current Jet Noise Prediction Capabilities,” AIAA–

2008–2933, 2008. 

7. ANSI S1.26-1995 (R2004), “Method for the Calculation 

of the Absorption of Sound by the Atmosphere”. 

8. Viswanathan, K., Alkislar, M.B., and Czech, M.J., 

“Characteristics of the Shock Noise Component of Jet 

Noise,” AIAA–2008–2835, 2008. 

9. Tam, C.K. W., Pastouchenko, N.N., and Viswanathan, K., 

“Computation of Shock Cell Structure of Dual Stream 

Jets for Noise Prediction,” AIAA–2008–27, 2008. 

Frequency (Hz)

PSD (dB)

102 103 104 10 75 5

80

85

90

95

100

105

AAPL Sideline

JNL Sideline

SP 50761, 60o

Frequency (Hz)

PSD (dB)

102 103 104 10 75 5

80

85

90

95

100

105

AAPL Sideline

JNL Sideline

SP 50761, 90o

Frequency (Hz)

PSD (dB)

102 103 104 10 75 5

80

85

90

95

100

105

AAPL Sideline

JNL Sideline

SP 50761, 120o

Frequency (Hz)

PSD (dB)

102 103 104 10 75 5

80

85

90

95

100

105

110

115

120

AAPL Sideline

JNL Sideline

SP 50761, 150o

NASA/TM—2010-216767 13 

Appendix—Jet Conditions Tested 

The tables below define the jet conditions tested and relate them to a “setpoint” number. The jet exit condition is defined in the 

first four digits in the setpoint number and the flight stream Mach number is defined by the last digit (Mfj = 0.0 is 0, Mfj = 0.1 is 1, 

and Mfj = 0.35 is 4). 

TABLE 2.—THE “COLD” JET CONDITIONS TESTED 

WHERE THE CORE STREAM COMBUSTOR 

WAS NOT USED SO THAT NTRc AND 

NTRf WERE NOT CONTROLLED 

[Note the last digit in the setpoint 

number indicates the flight stream 

Mach number (Mfj)]. 

Setpoint NPRc NPRf

50500/1/4 1.600 2.000 

50510/1/4 1.800 2.000 

50520/1/4 2.000 2.000 

50530/1/4 1.600 2.200 

50540/1/4 1.800 2.200 

50550/1/4 2.000 2.200 

50560/1/4 2.200 2.200 

50570/1/4 1.600 2.400 

50580/1/4 1.800 2.400 

50590/1/4 2.000 2.400 

50600/1/4 2.200 2.400 

50610/1/4 2.400 2.400 

50620/1/4 1.600 2.500 

50630/1/4 1.800 2.500 

50640/1/4 2.000 2.500 

50650/1/4 2.200 2.500 

50660/1/4 2.400 2.500 

TABLE 3.—THE “HOT” JET CONDITIONS TESTED 

[Note that heat was only added to the core stream and 

the fan stream temperature (NTRf) was not controlled]. 

[Note the last digit in the setpoint number indicates 

the flight stream Mach number (Mfj)]. 

Setpoint NPRc NPRb NTRc

TTc/Tamb

50200/1/4 1.600 2.000 2.310 

50210/1/4 1.800 2.000 2.310 

50220/1/4 2.000 2.000 2.310 

50230/1/4 1.600 2.200 2.310 

50240/1/4 1.800 2.200 2.310 

50250/1/4 2.000 2.200 2.310 

50260/1/4 2.200 2.200 2.310 

50700/1/4 1.600 2.400 2.310 

50710/1/4 1.800 2.400 2.310 

50720/1/4 2.000 2.400 2.310 

50730/1/4 2.200 2.400 2.310 

50740/1/4 2.400 2.400 2.310 

50750/1/4 1.600 2.500 2.310 

50760/1/4 1.800 2.500 2.310 

50770/1/4 2.000 2.500 2.310 

50780/1/4 2.200 2.500 2.310 

50790/1/4 2.400 2.500 2.310 
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Data Quality Assurance for Supersonic Jet Noise Measurements 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

The noise created by a supersonic aircraft is a primary concern in the design of future high-speed planes. The jet noise reduction 

technologies required on these aircraft will be developed using scale-models mounted to experimental jet rigs designed to simulate the 

exhaust gases from a full-scale jet engine. The jet noise data collected in these experiments must accurately predict the noise levels produced 

by the full-scale hardware in order to be a useful development tool. A methodology has been adopted at the NASA Glenn Research Center’s 

Aero-Acoustic Propulsion Laboratory to insure the quality of the supersonic jet noise data acquired from the facility’s High Flow Jet Exit 

Rig so that it can be used to develop future nozzle technologies that reduce supersonic jet noise. The methodology relies on mitigating 

extraneous noise sources, examining the impact of measurement location on the acoustic results, and investigating the facility independence 

of the measurements. The methodology is documented here as a basis for validating future improvements and its limitations are noted so that 

they do not affect the data analysis. Maintaining a high quality jet noise laboratory is an ongoing process. By carefully examining the data 

produced and continually following this methodology, data quality can be maintained and improved over time. 

15. SUBJECT TERMS

Aerodynamic noise 

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