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A NOVEL ONSITE NARROWBAND TOOL FOR FLIGHT TEST ACOUSTIC DATA PROCESSING

Michael Czech Stefan Uellenberg

Eric Nesbitt Yahia Abdelhamid

The Boeing Company

Seattle, WA

ABSTRACT

Flyover noise tests are undertaken to assess the noise levels of a given airplane at both approaches and takeoffs with a number of microphones mounted on the ground underneath the flight path. In the past the noise analysis procedure involved the data from a single microphone processed to 1/3-octave band noise spectra off-site, which made it difficult to identify tones or amplitude variations with emission angle. This paper introduces a narrowband tool for the analysis of aircraft flyover noise and discusses the challenges due to the non-stationary character of the source as well as the need to ensemble average the data. In addition, procedures and set-up for a noise flight test are outlined and example noise contour plots presented. Results show a dramatic improvement of the processed noise data quality where individual tones are clearly distinguishable, thus aiding the identification of source locations .

INTRODUCTION Noise regulations have been increasing considerably driven by local communities living in the vicinity of an airport. Consequently, the noise of an aircraft, much like its range and payload capability, has become a competitive factor as the noise of an aircraft determines landing fees as well as landing rights. The Boeing Company and Rolls Royce have teamed up to study selected noise reduction concepts as part of the Quiet Technology Demonstrator program (QTD). Those concepts, evaluated in wind tunnel tests and computational simulation, were to be validated in a flight test. Nesbitt et al (2002) further discuss the scaling effects from wind tunnel tests to flight test for such noise reduction concepts.

Copyright 2002 by The Boeing Company. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission.

Traditionally, flight tests are undertaken to evaluate the noise levels and sources of a particular airplane. Those tests have mainly been carried out under certification procedures complying with the requirements specified in FAR 36 and ICAO Annex 16. In this context the airplane would perform a series of takeoff and approach flight path intercepts over a microphone array for a number of power settings.

Mics being monitoredfor extraneous noise

contamination

SidelineMicrophones

Under Flight Path Microphone Array

Flight Track

Test Airplane

Runway

Flight Intercept

Receive Conditioned FlightPath Data

Start Flyover Data Analysis

Noise Analysis Completed.Narrowband Normalized and

Averaged Data to Plotting

Plots ofNoise

SpectraDisplayed on

Screen

Mics being monitoredfor extraneous noise

contamination

SidelineMicrophones

Under Flight Path Microphone Array

Flight Track

Test Airplane

Runway

Flight Intercept

SidelineMicrophones

Under Flight Path Microphone Array

Flight Track

Test Airplane

Runway

Flight Intercept

Acquire Data

Figure 1. Schematic of flyover racetrack. A typical flyover is schematically shown in Figure 1 where the airplane repeats a defined flight track for a number of power settings. The airplane is setting up for a power setting as it approaches the acoustic test range. Flight path angle and throttle settings are subsequently fixed as the airplane passes over the centerline microphone stations. The test airplane will change engine power for a “go-around" to the next test condition. A typical flyover takes about 10 minutes to complete, leaving only very little time to process all of the microphone data on site. While the acquisition of the data is done online at the flight test site, the actual data processing and analysis has been an offsite process in the past that occurs well after the completion of the flight test. Furthermore, data analysis was limited to one-third octave spectra from which noise indices such as PNLs and EPNLs were computed. These results were computed form data acquired by only one

8th AIAA/CEAS Aeroacoustics Conference & Exhibit<br> <font color="green">Fire17-19 June 2002, Breckenridge, Colorado

AIAA 2002-2502

Copyright © 2002 by the author(s). Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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microphone at a 0.5s integration time. A typical 1/3-octave contour plot is shown in Figure 2 where SPL is plotted over frequency bands and emission angle.

Figure 2. 1/3 octave approach noise contour from centerline microphone array. It is difficult to distinguish individual tones form 1/3-octave data and the long integration times contribute to strong smearing effects. Hence, the necessity arose to develop a narrowband noise data analysis system to better assess the prevailing noise sources and to evaluate the efficiency of new noise technology concepts. During a typical flyover the airplane becomes a non-stationary noise source and it becomes necessary to maintain short integration intervals as to avoid data smearing. Unfortunately, shorter integration times result in lower accuracy in the statistical estimate of the power spectral density, especially at lower frequencies. This can be overcome by averaging the data from several microphones. Gridley (1982) and Kelly (1993) have addressed the issues of ensemble averaging and signal processing of non-stationary sources for constant altitude flyovers only. Although Gridley (1982) goes beyond the signal processing concerns and establishes a total package for narrowband ensemble averaging, the proposed concepts lacked the ability to project the acquired data to an arbitrary flight path at standard day conditions. However, this is instrumental in making qualified comparisons between data acquired over a span of time under varying flight conditions. An additional requirement imposed on the narrowband system was to make the processed noise information available within minutes upon completion of a flyover. This would provide immediate data visibility during the

flight test conduct. The main requirements for the development of the narrowband analysis tool were:

• Data analysis, screening and plotting cycle time < 5 min

• Optimization of signal processing parameters such as integration times and number of averages.

• Ensemble averaging of microphone array • Data extrapolation to standard day conditions

and normalized flight paths This paper describes the development of the narrowband tool and focuses on its main components and features. The experimental set-up for the noise technology flight test is described subsequently explaining how flight test data is generated and processed. Finally, results are presented for a typical flyover demonstrating the high resolution of engine tones typical of high bypass ratio engines.

TOOL DEVELOPMENT Three sets of data, namely performance, weather, and acoustic data, become available immediately after the airplane has completed a microphone flyover. The performance data is acquired on the airplane and contains airplane positioning as well as engine cycle parameters as a function of time. The weather data contains pressure, temperature and relative humidity as a function of altitude acquired by a special metrological airplane as well as a ground weather station. The acoustic file contains the recorded spectra for each microphone as a function of observer time. All three datasets are then utilized in calculating a set of noise spectra as a function of emission angle, for an arbitrary flight path corrected to standard atmospheric conditions. Main aspects of the tool are

• Determine the actual source location due to non-stationary effects

• Average noise spectra for all microphones at equal source emission angles

• Account for atmospheric absorption and spherical divergence

• Extrapolate data to standard conditions and standard flight path

In addition, a method had to be found to limit the number of spectra which had to be processed during one flyover without reducing the quality of the data.

Frequency bands

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Non-stationary source effects and spectra averaging As the airplane is moving along a prescribed flight path, one may consider a small increment of time during which the data is assumed relatively or locally stationary. According to J.S. Bendat and A.G. Piersol (1980), a time series analysis can be employed, provided that the statistical properties within these time increments remain unchanged to a large degree. While the source characteristic becomes increasingly stationary with smaller integration time, the statistical uncertainty of the energy spectra approaches unacceptable levels. It is straightforward to argue that the quality of the obtained energy spectra is a function of the number of averages of such spectra. For a given bandwidth and cut-off frequency one may either sample a time N*∆t and average N parts of the acquired data or sample only over a time ∆t but with N microphones as illustrated in Figure 3. The use of only one microphone using a temporal averaging approach would yield an unreasonable long sampling time and strongly smeared data. Therefore, several microphones must be employed to yield a good spectral estimate and this may be termed a spatial averaging. An approximate estimate of the spectral estimation error is:

st NN1

=ε

where Nt=number of temporal averages, Ns=number of spatial averages or microphones

∆t

1 2 3 4 Ν−1 Ν

Ν∆t

Single Mic

Array of N Microphones

∆t

1 2 3 4 Ν−1 Ν

Ν∆t

Single Mic

Array of N Microphones Figure 3. Schematic of ensemble averaging

Adequate integration times had to be selected for approach and takeoff conditions based on maximum frequency, frequency bandwidth and number of frequency bands. A series of integration times and temporal averaging schemes were tested in a laboratory environment. Previous flight test data from a microphone array consisting of 6 microphones were played back through the analyzer and processed with

various bandwidths and temporal averaging combinations (Table 1). A minimum integration time of 0.107 seconds was selected which corresponds to a 9.375 Hz bandwidth.

Table1. Test Matrix for minimum integration time and bandwidth determination Early on it became apparent that smaller integration times were necessary to resolve the approaches than the takeoff conditions. In the case of the takeoffs an integration time of 0.32 seconds was selected with 3 temporal averages giving a total of 18 averages when employing 6 microphones. This yields a spectral estimate uncertainty of about +/-2dB with a 90% confidence. Figure 4 is an example for a takeoff measurement with the red line depicting the sound pressure level from a single microphone with 3 temporal averages and the blue line showing the ensemble averaged result for the same emission angle. The data illustrates the much improved signal to noise ratio for the ensemble averaged data, in particular at the higher frequencies.

Figure 4. Comparison of single and averaged microphone data for a typical flyover.

Cond min int.time

Bandwidth

Averaging int.time

Bins MaxFreq.

Duration #ofSpectraper Mic

#ofSpectraTotal

[sec] [Hz] [sec] [Hz] [sec]

App

roac

h 0.16 6.25 1 0.1600 800 5000 40 250 60000.16 6.25 2 0.3200 800 5000 40 125 3000

0.107 9.35 1 0.1070 800 7480 40 374 89760.107 9.35 2 0.2139 800 7480 40 187 44880.107 9.35 3 0.3209 800 7480 40 125 2992

Tak

eoff 0.16 6.25 1 0.1600 800 5000 60 375 9000

0.16 6.25 2 0.3200 800 5000 60 188 45000.107 9.35 1 0.1070 800 7480 60 561 134640.107 9.35 2 0.2139 800 7480 60 281 67320.107 9.35 3 0.3209 800 7480 60 187 4488

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Source location One important aspect is to correlate the observer time or microphone time to the aircraft location where the noise was emitted. The noise signal travels from the source to the observer at the local speed of sound and arrives at the microphone at time t. The travel time from the source to the observer (Figure 5) is commonly referred to as the retarded time t . The source location at source time t-t is reconstructed from the airplane performance data and a position vector is drawn from the source at time t-t to the observer. The emission angle relative to a specific microphone is then obtained and noise spectra are recorded as a function of emission angle.

Flight path(flown)

Observer location

Power spectrum at time t

Moving Source

Emission angle α SPL

Flight path(standard)

α

α

at time tat time t - τ

Figure 5. Schematic of extrapolation to normalized flight path and the non-stationary source characteristic.

For a straight and level flyover, each microphone from the linear array should theoretically record identical spectra only offset by a small time increment. However, flyovers are undertaken at various flight path angles and each microphone will record data at slightly different noise emission angles. A linear interpolation was, therefore, applied to the computed spectra to obtain identical emission angles for all microphones and this will allow ensemble averaging of the data. Atmospheric absorption model The sound amplitudes are attenuated due to atmospheric absorption as a function of frequency, distance and the absorption coefficient. In turn, this coefficient depends on the humidity, temperature and pressure of the atmosphere. Shields and Bass (1977) have developed a weather model to compute absorption coefficients in dB/meters for a given frequency and weather condition. They modeled the absorption coefficient as a sum of viscous effects as well as rotational and vibrational relaxation effects. The individual components were formulated as functions of barometric pressure, temperature and relative humidity.

The software computes the absorption coefficients for the weather conditions of the flyover for a number of layers. It is then possible to determine the level of absorption for a given acoustic signal moving through a finite number of layers between source and observer. Extrapolation to standard conditions Day to day variations of weather conditions make it necessary to extrapolate the acquired data to standard weather conditions as defined in FAR36. In addition, the airplane does not fly repeatedly the same flight path due to changes in weight, power setting and common flight control limitations. Hence, the flown flight path is extrapolated to a common flight path as defined by FAR36 regulations and illustrated in Figure 5. The change in acoustic distance is accounted for and the emission angle is kept constant. The results are written as power spectra at the observer location for a standard day arising from a point source moving along a standard flight path. Spectra number limitation Assuming that the required “on condition“ times for the approach and takeoff would be 40 seconds and 60 seconds, respectively, the total number of spectra becomes unmanageable. As shown in Table 1, a single approach condition can require a total number of spectra as high as 8976 for all 24 microphones and this greatly exceeds the available processing time window of 5 minutes per flyover. As the airplane flies over the microphone array, the required processing times can be significantly reduced by normalizing and ensemble averaging spectra corresponding to preferred emission angles. It was found that it is sufficient to analyze the data in 5 degree emission angle intervals which reduces the number of spectra to only 528. The time was determined a priori from the airplane flight path at which the source location would yield a pre-defined emission angle. Acoustic data was then only processed for this particular event and was available for discussion within a few minutes after the flyover. The acoustic benefit of the test hardware, be it the inlet or the jet noise suppression devices could be quantified within a few minutes after the airplane flew over the microphone array. All of the acoustic data was reprocessed by the end of the day using the entire set of microphone spectra for every microphone.

FLIGHT TEST SET-UP The Noise Flight test was conducted at the Glasgow Industrial Airfield, a deactivated Air Force SAC Base, located near Glasgow, Montana (Figure 6). The countryside is generally flat to rolling with ground cover consisting primarily of range grass.

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Figure 6. Aerial photograph of the flight test site. Data was acquired while the airplane performed a series of takeoff and approach flight path intercepts over the microphone array for a number of hardware configurations. The test airplane flight profile (climb angle and pitch angle) and weight were allowed to vary as appropriate to control airplane performance and position over the microphone array at each power setting. The overall data acquisition process utilized time synchronized measurements of the following:

• Meteorological measurements • Airplane performance and engine parameters. • Airplane space position. • Freefield acoustic data.

All data was transmitted to the test control center. Weather data The Upper Atmospheric Weather System was installed in a light airplane (MET airplane). This system consists of a dew point sensor, a total temperature probe and transducers for static and dynamic pressure. Upper atmospheric weather data was sampled during the airplane ascent. The sampling rate was set depending on the climb rate of the airplane and the altitude/readout resolution desired for the test. Data was transmitted to the test control room using a telemetry channel where Temperature, dew point, relative humidity and noise absorption values were computed as a function of altitude. The upper air weather data was acquired prior and after a given set of flyovers. Linear interpolation to this data provided the required weather information for the time of any flyover. In addition, a 10-meter weather system, geographically close to the microphones, was utilized to measure wind

speed, wind direction, pressure, ambient temperature, and dew point temperature. The general guidelines for the wind speed limits are 12 knots total and 7 knots crosswind components. Figure 7 shows a typical weather plot of the variation of the absorption coefficient of an 8000Hz wave with relative humidity and temperature. The weather data, acquired at several altitudes covering the flyover altitude range, had to remain in the limits indicated by the solid lines for a condition to be considered good.

40

35

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25

20

15

10

5

0 100 90 80 70 60 50 40 30 20 10 0

Tem

pera

ture

(D

egre

es C

)

Relative Humidity (%)

Region of Acceptable Test Conditions

16 dB/100 meters @ 8000 Hz

14 dB/100 meters @ 8000 Hz

12 dB/100 meters @ 8000 Hz

Acoustic Standard Day

4.88 dB/100 meters @ 8000 Hz

Figure 7. Typical weather plot illustrating the measured atmospheric absorption.

Performance data Space/time airplane tracking was achieved by utilizing the real-time differential Global Positioning System (DGPS). Real-time Differential GPS uses two GPS stations: a fixed reference station and a roving station. The reference station is located on a precisely surveyed site. The roving station is located on the test aircraft. The reference station calculates its position based on the GPS satellites. This position is compared with the reference station’s actual location to determine satellite range corrections data, which is sent to the roving station via a real-time data link. The roving station then combines its GPS satellite data with the satellite range corrections data to accurately determine its position. A data system on the airplane will use GPS data to determine airplane position with respect to the microphone array. GPS antenna position was translated to test airplane main-landing-gear extended bottom of-the-wheels position. Airplane position (time correlated altitude and sideline offset) relative to twenty four ground stations identified under the airplane's flight path was telemetered down to the test control room.

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Acoustic data Groups of six Brüel and Kjær microphones were in a Boeing-built “Flush-Dish” (ground-plane microphone) under-the-flight-path test location and on each sideline. The terrain, in the vicinity of the microphones, complies with the requirements specified in FAR 36 and ICAO Annex 16. Figure 8 shows the arrangement of the 6 center-line microphones. Additional microphones were installed at locations +/-1475 feet parallel to the center line.

Figure 8. Aerial photograph of the end of the runway and the center-line microphones.

Grass was mowed in the vicinity of the microphones. For the flush dish installation a circular area 25 feet in diameter, centered on the microphone, was rototilled, cleared of large rocks, raked smooth and rolled firm. The flush dish petals were meshed into the soil so that they appear as an extension of the ground plane (i.e., the complete circumference of each dish was in contact with the soil). A typical microphone installation with applied wind screens is shown in Figure 9. Brüel and Kjær microphone cartridge with protective grid cap were attached to the microphone preamplifier. The battery-operated microphone power supplies are Boeing-designed. The signal output circuit included a low impedance network to drive long cables and minimize high frequency roll-off. Signals from the microphone systems were input to a 32 channel recording system which has a have a sampling rate of 48 KHz and include anti-aliasing filters.

Figure 9. Photograph of a microphone mounted in a specially shaped dish to minimise edge reflection.

Based on a predetermined estimate of the maximum overall sound pressure level at each microphone location, the amplifier gain for each channel was preset to produce a record level between -3 and -13 dB relative to 1 VRMS at the peak noise of the flyby. An ambient noise recording was made for at least 10 seconds prior to the aircraft flyby. Condition number and gain settings were recorded on a log record for each airplane flyover. As the airplane passes over the microphone array, each test point was monitored for an optimum signal level of -3 to -13 dB relative to 1 VRMS. The condition was terminated when the acoustic signal has been recorded at sufficient length to define airplane noise levels to at least 10 PNdB down from the maximum level. Noise data screening During community noise testing, it was necessary to monitor the microphone signals from the various test point locations to evaluate the ambient noise level and ambient events (e.g., bird songs, other aircraft, cars, air conditioners, etc.) that may occur during data acquisition. As an aid to identification of these events and their potential to affect test data, a nominally calibrated, real-time spectral display of this audio data was done with a real time spectrogram system. The results of this activity was an improved confidence in the acoustic data acquired and helped in deciding whether a condition was called good or bad. Potential ambient noise interference problems were logged to assist the data processing. Flyover data processing On a narrow band data system, 800-point narrowband spectra were acquired from the analyzers for each 107 millisecond period (approaches) or 320-millisecond

Microphone

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period (takeoffs) using a frequency range of 0 to 7500 Hz (9.375 Hz resolution). When the flyover was completed, the SPL values were corrected for gain setting, frequency responses (interpolated as necessary for narrowband data), and end-to-end absolute sensitivity. The resulting “as-measured” spectral time histories for each data channel were stored in a relational database (one on each data system) from which they were read and output in RDS (Readable Data Standard) format to the narrowband data analysis systems. The noise data, performance and weather data was then read into the narrowband analysis software. The extrapolated data was screened and ensemble averaged. Contour noise plots were available for analysis in less than five minutes after the flyover.

RESULTS

All of the contour plots presented, vary over a range in emission angles from 30 to 150 deg. The frequency range shown in these plots is selected to show at least the second harmonic of the blade passage frequency. The scales have been removed due to the proprietary nature of the engine noise measurements. Figure 10 shows a contour plot of narrowband processed flyover noise data from a single flush dish microphone under the flight path. SPL data are plotted against emission angle and frequency.

Figure 10. Noise contour plot of narrowband data acquired by a single microphone.

The scale ranges from low SPL data, shown in blue, to high SPL data shown in white. The data shows clearly the noise to be concentrated at the lower frequency range with occasional drop-outs. The drop-outs occur if the spectral content for a given frequency band falls below the noise floor. However, the results demonstrate that it is very difficult to detect spectral details, such as tones, and the statistical uncertainty is clearly too high. In contrast, the ensemble averaged data from 6 center line microphones in Figure 11 show a very good resolution of tones. The tone frequencies decrease with emission angle due to the Doppler effect. The blade passage tone is clearly distinguishable from the broadband noise and is of relative constant strength over a wide range of emission angles. It is also interesting to note that higher harmonics of the blade passage tone are distinguishable.

Figure 11. Noise contour plot of ensemble averaged narrowband data for an approach.

A minor tonal noise source was identified on the 777 airplane during the 1995 certification flight test and it was suspected to arise from an airframe source. Although this tone contributed very little to the total aircraft EPNL level, it made an interesting case study for the narrowband analysis tool. During the recent flight test the tone was identified as emanating from the wing thermal anti-icing vents. A comparison between single microphone data and ensemble averaged data in the aft arc is shown in Figure 12.

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Frequency – Log scale

Frequency – Log scale

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Ensemble Average without Tone

80

70

60

50

40

30

207000600050004000300020001000

Single MicrophoneEnsemble Average with Tone

0 Figure 12. Single microphone results for an experimental approach condition. The hay stacking of the tones and the appearance of multiple tones can be attributed to changing flow conditions along the span of the wing leading edge. This phenomenon is evident from the single and averaged microphone data measurements, although the ensemble averaged data demonstrates a significant signal to noise ratio improvement. Upon taping the wing thermal anti-icing vents and retesting the airframe conditions the tones disappeared. The ensemble averaged contour plot in Figure 13 shows the resulting microphone directivity before and after applying tape over the vents. The onsite processing of the acquired noise data made it possible to immediately evaluate changes to the noise source. These findings led to recent laboratory experiments at Boeing and solutions have already been identified that would eliminate this noise source. These design changes were subsequently implemented on the current production airplane in support of continuous product improvement

Figure 13. Comparison between ensemble averaged noise spectra with and without 2000Hz tone. Results from a takeoff at a cutback power setting are shown in Figure 14. The blade passage tone and its higher harmonics are found to emerge from the

broadband noise. These fundamental fan tones at moderate to high frequencies can give rise to a significant EPNL increase, due to the added tone emergence penalties and noy weighting. The results also show the presence of multiple pure tones, or Buzzsaw tones, predominantly shown to concur with low emission angles. This forward radiating phenomenon occurs while the fan is operating at supersonic relative tip velocities creating weak shock structures at each blade tip element spiraling upstream. Figure 14 also shows a low frequency tone that may be attributed to the combustor resonance or an engine vibration. The presence of jet noise is shown in the aft arc at low frequencies as seen in the top left corner of Figure 14. It should be mentioned that the peak jet noise occurred during the highest takeoff power setting and is the dominant noise source for such a condition. Nesbitt et al (2002) present a more detailed account on jet noise sources, i.e. the effect of various jet noise suppression device. All of their presented data utilized this narrowband ensemble averaging process as described at great lengths in this paper.

Figure 14. Noise contour plot of ensemble averaged narrowband data for a takeoff.

CONCLUSIONS This paper has discussed the development and application of a novel narrowband analysis tool for aircraft flyover noise data. The signal processing issues of analyzing non-stationary data have been addressed and the need for ensemble averaging illustrated. In addition, a detailed description of a recent technology

Frequency – Log scale

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flight test is given where the procedures of acquiring noise, performance and weather data are outlined. The data acquired is normalized to a standard flight path and standard weather conditions to allow qualified comparisons between results taken on different days or flown at various flight path angles. Results from this flight test demonstrate the much improved data visibility compared to the traditional 1/3 octave band process. Engine tones and their variation with emission angle are very well resolved and allow a more informed analysis of such data. During this flight test seven different hardware configurations were tested. After the successful completion of each approach and takeoff power-line the acoustic data became the crucial key in the selection of the next configuration to be tested. It helped in establishing the most logical test sequence and resulted in the omission of at least one unnecessary configuration. The application of this tool will help in the location of noise sources and, consequently, aid in the reduction of aircraft noise.

ACKNOWLEDGEMENTS The authors would like to thank the support of Rolls Royce for their many contributions to this project. We would also like to thank all of the Boeing staff that had worked very hard especially on site to make this flight test a reality.

REFERENCES 1 E Nesbitt, R Elkoby, S Bhat, P Strange, C Mead,

“Correlating Model-Scale & Full-Scale Test Results of Dual Flow Nozzle Jets”, AIAA 2002-2487,2002

2 D Gridley, “Program for narrow band analysis of aircraft flyover noise using ensemble averaging techniques”, NASA Contractor Report 165867, 1982

3 J. J. Kelly, "Signal processing of aircraft flyover noise", J Sound and Vibration, 160(3), pp485-501,1993

4 D. Shields, H. Bass, “Atmospheric absorption of high frequency noise and application to fractional-octave bands”, NASA CR-2760, 1977

5 J Bendat and A Piersol, “Engineering applications of correlation and spectral analysis”, John Wiley&Sons, 1980, pp1-38