Proceedings of Meetings on Acoustics - … · More recently, the Groupe d’Acoustique de...

Proceedings of Meetings on Acoustics

Volume 19, 2013 http://acousticalsociety.org/

ICA 2013 Montreal

Montreal, Canada

2 - 7 June 2013

Signal Processing in AcousticsSession 4aSP: Sensor Array Beamforming and Its Applications

4aSP8. Fly-over aircraft noise measurement campaign at Montreal-Trudeau airportusing a microphone arrayJean-Francois Blais*, Cédric Camier, Mathieu Patenaude Dufour, Robby Lapointe, Jonathan Provencher, Thomas Padois, Philippe-Aubert Gauthier and Alain Berry

*Corresponding author's address: Acoustics and Vibration, Bombardier Aerospace, P.O. Box 6087, Station Centre-ville, Montreal,H3C 3G9, Quebec, Canada, [email protected] Engines being quieter due to high by-pass ratios, the airframe noise, produced for instance by landing gears or high-lift devices, has become asignificant contributor to the total noise radiated by aircraft during approach and landing. As part of the investigations carried out to understandnoise generation mechanisms, the beamforming techniques developed over the last decade and applied to microphone array measurements haveshown to be effective tools for localization and quantification of these aerodynamic noise sources. In order to validate their in-housebeamforming softwares, Bombardier Aerospace and the Groupe d'Acoustique de l'Université de Sherbrooke have conducted a 5-daymeasurement campaign in June 2012. The 95-microphone array was located on the roof of a building next to the Montreal-Trudeau airport.Aircraft position was determined by two high-definition cameras, both synchronized with the microphone array by inter-range instrumentationgroup time codes generators. This paper summarizes the measurement campaign. The aircraft tracking tool and the beamforming algorithmsused to characterize the noise sources are presented. Several Bombardier CRJ fly-overs were recorded during this test. Beamforming resultsobtained for different airlines are compared in order to evaluate the repeatability of the method.

Published by the Acoustical Society of America through the American Institute of Physics

Blais et al.

© 2013 Acoustical Society of America [DOI: 10.1121/1.4800879]Received 22 Jan 2013; published 2 Jun 2013Proceedings of Meetings on Acoustics, Vol. 19, 055084 (2013)

INTRODUCTION

Air traffic noise is an important source of annoyance to which communities close to major airports are exposed on a daily basis. In-line with its objectives of designing more environmentally focused products to meet the evolving needs of its customer, Bombardier Aerospace (BA) has funded in the last years research and development projects to study and reduce the noise from its aircraft.

Latest engines being quieter due to high by-pass ratios, the airframe noise, produced for instance by landing gears or high-lift devices, has become a significant contributor to the total noise emitted by aircraft during approach and landing. As part of the investigations carried out by BA to understand aerodynamic noise generation mechanisms, a first series of microphone array measurements was done in November 2005 on a Global Express, an ultra-long range business jet.1 This method was found to be a suitable tool to map with a good spatial resolution the main sources of noise due to turbulence and vortices created by the airflow around high-lift devices, landing gears, and flow excited acoustic resonators. A qualititative assessment of the sources’directivity was also done.

More recently, the Groupe d’Acoustique de l’Université de Sherbrooke (GAUS) and Bombardier Aerospace worked together on their beamforming capabilities by implementing different algorithms developed over the last decade that have shown to significantly improve localization and quantification of aerodynamic noise sources. In the scope of this project, a 5-day measurement campaign was conducted in June 2012 at the Montreal-Trudeau airport. Such test location allowed to gather fly-over data from several Bombardier CRJ200 aircraft of different airliners, their position relative to the array being tracked by two high-definition cameras.

This paper focuses on the measurement campaign. Beamforming algorithms used to characterize the noise sources are first summarized. In the second section, the microphone array setup and aircraft tracking tools are presented. Beamforming results obtained for two CRJ200 aircraft are finally compared and discussed in order to evaluate the repeatability of the method.

BEAMFORMING METHODS

Beamforming consists in using a large number of microphones (microphone array) to focus on sound coming from a specific direction. In previous work done at BA,1 a time domain delay-and-sum beamforming technique had been implemented to process the phased array data. With this method, acoustic pressure signals pi(t) measured by each microphone of index i are delayed by appropriate amounts and added together to reinforce the signal emanating from a specific region in space, as schematized in Figure 1.

Let’s consider a focus point r at a distance Ri(r) from each microphone of the array. Considering propagation from monopole sources, the time delay can be written as2

cRii )(rR �� (1)

where c is the sound velocity. Note that for a moving source, Ri(r) is a function of time. The output B(t,r) of the delay-and-sum beamforming applied to an array of N microphones is given by

.)()(1),(1��

��N

iiii tpR

NtB Rrr � (2)

In order to reduce computational time, the present paper explores methods written in the frequency domain. Let’s first define the steering vector g, for which components gi are the pressure amplitudes at the microphone locations of a monopole source with unit strength are

.)(2 rRi

fji Reg i�� (3)

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Proceedings of Meetings on Acoustics, Vol. 19, 055084 (2013)

FIGURE 1. Beamforming method representation

The source auto-power at a focus point r can be written as

4g

Cgg�

�A (4)

where C is the cross-spectral matrix defined as �� ppC

21 (5)

and p is a vector for which components pi(f) are the measured complex pressure amplitudes. The asterisk (*) denotes the complex conjugate. This method is known as “Conventional Beamforming” (CB).

In windy conditions (wind-tunnel, outdoor measurements, etc), microphone auto-power levels are often much higher than the corresponding cross-power.3 In such environment, there are pressure disturbances that are added to the acoustic pressure measured by the microphones. Wind noise is generally incoherent from one microphone to the other and will therefore only in the auto-spectra, i.e.. Accordingly, the diagonal elements of matrix C defined in Eq. (6) are set to zero in order to obtain “cleaner” maps. This approach that disregards the auto-correlation (AC) terms of matrix C is referred to as CB without AC.

In the case there is only one source in the focal plane, the output of the beamforming processing (the acoustic map) is called the point spread function (PSF) or beampattern. Its properties such as the beamwidth and the maximum sidelobes (that could be misinterpreted as secondary sources) depend on the source frequency, the array’s geometry and the focal distance.

For more complex phenomenon, such as aerodynamic noise, beamforming results consist in a map of all acoustic sources convoluted by PSFs computed for various space directions. In order to obtain cleaner maps, that is to say with a refined localization of sources and no phantom images, deconvolution methods have been proposed in the literature.3-5,8 CLEAN-PSF3 is one of these methods applied in the present paper. Let’s consider the beamforming response computed with Eq. (5) and represented on a map (called “dirty map”). The CLEAN-PSF algorithm can be summarized as follow:

1. Maximum amplitude on the dirty map, noted pmax, is localized is (xmax, ymax);2. Theoretical PSF of a monopole of amplitude pmax located in (xmax, ymax) is computed and substracted

from the dirty map to obtain a new dirty map; 3. A “clean map” is built in parallel with a monopole of amplitude pmax located in (xmax, ymax);4. Steps 1 to 3 are repeated until a maximum number of iterations or a specific criterion are reached.

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EXPERIMENTAL SETUP

Microphone Array

A custom-made measurement system developed by the GAUS including a 95-microphone array with its related conditioning and recording system was used for the test. The microphone array was installed on the roof of a building next to the Montreal-Trudeau airport, as shown in Figure 2 a). Prior to BA’s first measurement campaign1,simulations had been performed to optimize the frequency range that the array could resolve by changing the spacing between the microphones and the extent of the array. A compromise had been found to distribute the microphones semi-randomly in an 8.5-meter diameter circle, with a pattern repeated five times. With this geometry, the frequency range the antenna could resolve extended from 500 Hz to 2500 Hz for a focus distance of 30 to 40 meters. Due to the limited space available on the building’s roof and considering that fly-overs at this location are at similar altitudes, the same microphone array configuration, shown in Figure 2 b), was selected.

The microphones were installed on a plywood platform, stiffened by wooden studs. The center of the platform was at 550 m from the beginning of the runway. Microphones were all mounted with their axes oriented perpendicularly to the flight-path at grazing incidence. Their membranes were assumed to be located in the reflection plane resulting in a doubling of the sound pressure.2 Location of each microphone was verified with a tape measure and a theodolite mounted on a tripod.

Aircraft Position Tracking Tools

Microphone array testing relies heavily on the accurate positioning of the aircraft as a function of time as it flies over the array. For this measurement campaign, two high-definition cameras were used to track the aircraft position. The first one was located on the side and the second one was oriented to look at the back of the aircraft. Both cameras were at about 100 m from the array. Wide-angle lens were used to make sure aircraft were completely visible in the videos. A similar approach had been used during the 2005 tests and compared to information collected by a Differential-GPS (DGPS) and an Inertial Reference System. It had been shown that the position and attitude of an aircraft flying at altitudes of about 30 m could be identified with a good precision (within 1.5 m).

FIGURE 2. (a) Microphone array, facing the runway and (b) array’s geometry

a)

b)

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FIGURE 3. Tracking cameras configuration

Figure 3 shows a diagram of a point A moving along in space with respect to the cameras and the center of the microphone array. The vectors L1 and L2, whose directions are expressed using the angles � and � given by the cameras, indicate the location of the tracked point A. These vectors need to be expressed in the coordinate systems of the cameras, which can then be transferred into the coordinate system of the array. This is done through a pure rotation transformation of the vectors’ coordinate system using the Euler angles. Coordinates (x, y, z) of point A can determined by using the following set of equations:

�

�

� 2211211

112122211

2211211

112122211

2211211

1212221

sincoscoscossinsincoscoscossin)(sincos

sincoscoscossinsincoscoscossin)(sincos

sincoscoscossinsincossinsin)(sincos

��

��

��

��

��

��

��

�

hhDhz

hhDDy

hhDx

(6)

Parameters D1, D2, h1 and h2 were determined using with a tape measure and a theodolite mounted on a tripod. In order to obtain angles � and � from recorded videos, cameras had to be calibrated. This has been done by randomly placing dots on a x-y plane drawn on a conference room’s wall as shown in Figure 4 a). The two perpendicular axes have been adjusted with a self-leveling laser. Each camera has been aligned with the origin matching the center of the picture. Measured angles � and � could be obtained from the position of the dots and the distance between the camera and the wall. Relation between angles � and �, and pixel coordinates has been derived with an order 3 polynomial function through a least-square error minimization. Calibration curves are shown in Figure 4 b).

D1

h1

h2

D2

�1

1

�2

2

L2

L1

Aft camera

Side camera

Microphone array reference

point

Tracked point A (x,y,z)

Building

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FIGURE 4. Camera calibration method: (a) calibration setup and (b) calibration curves.

Aircraft reference plane used as the focal distance for beamforming calculations has been determined by tracking the nose and main landing gears. The tracking method applied to a CRJ200 with an in-house software is shown in Figure 5. In this example, the point being considered is the nose landing gear.

Synchronization of the microphone array measurements and the two cameras was achieved with three inter-range instrumentation group (IRIG-B) time codes generators. Two were connected to the audio input of the video cameras and one was the 96th channel of microphone array recording system. A high resolution camera was also used to take pictures of aircraft from below in order to superimpose them to the beamforming mapping results.

RESULTS

In this paper, a special attention is paid to the CRJ200 noise levels. The A-weighted spectrum recorded by one of the array’s microphones for a CRJ200 fly-over is shown in Figure 6. It can be seen that one of the main noise components is a tone around 470 Hz. This has also been observed in Reference 7. This tone is due to the overpressure relief valves (OPRV) located under both wings and that behave like resonators. It is typical of CRJ200 noise signature.

The tonal noise radiated by the OPRV of two CRJ200 from two airliners (labeled A and B) is compared in Figure 7 for aircraft directly above the array. Aircraft A altitude and speed were 32 m and 65 m/s while aircraft B’s were 40 m and 62 m/s. Note that superimposed pictures are the same since one of the image was not available. Axes shown in Figure 7 are in the aircraft’s referential.

Two beamforming algorithms are also compared: conventional beamforming and CLEAN-PSF, both without auto-correlation. Acoustic signals have been propagated to the focal plane defined by the nose and main landing gears. The acoustic maps have been produced on 100 × 100 grids by integrating the tonal component of the spectrum between the two red dashed lines in Figure 6. Absolute levels have been normalized with respect to the maximum of the four maps.

With both beamforming methods, OPRV are properly identified. CLEAN-PSF shows however a significant improvement over conventional beamforming in terms of localization. Slight misalignments (similar for both aircraft) with approximate location of the actual OPRV (indicated by crosses in Figure 7) might be caused by the wind or aircraft positioning errors. Knowing the exact location of the valves, acoustic maps could be realigned with the pictures for studies in other frequency bands.

In terms of absolute levels, there is a 2 dB difference between the noise level produced by the OPRV of the two CRJ200. Although aircraft were flying at approximately the same speed, other parameters such as the angle of attack of the aircraft or positioning errors. Further analyses with a larger number of aircraft would be the next step for a better understanding of the noise level differences.

a)b)

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FIGURE 5. Position tracking of a CRJ200.

FIGURE 6. A-weighted frequency spectrum recorded by one of the microphones for a CRJ200 flying over the array.

100 1000 10000

SPL [

dB

(A)]

Frequency [Hz]

5 dB(A)

OPRV Tone

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FIGURE 7. Beamforming results using CB w/o AC and CLEAN-PSF w/o AC for two CRJ200.

CONCLUSION

In this paper, a microphone array measurement campaign conducted by Bombardier Aerospace and the GAUS was presented. A few beamforming algorithms used to characterize the sources of noise on landing aircraft were summarized. The experimental setup was detailed, with an emphasis on the aircraft position tracking tools. Conventional beamforming and CLEAN-PSF, both without auto-correlation, were compared in the localization process of the overpressure relief valves of two CRJ200. The CLEAN-PSF method showed good improvements over the CB to localize such small noise source. The absolute levels produced by the OPRV and measured on both aircraft were in the same order of magnitude. A first extension of this work would be to compare the algorithms presented in this paper to other deconvolution algorithms such as CLEAN-SC2, DAMAS3, orthogonal beamforming4

or DAMAS-MS7. Considering the large number of CRJ200 that were recorded during the test, a statistical analysis of the results in terms of absolute acoustic levels should also be performed for more emission angles.

ACKNOWLEDGMENTS

This work has been supported by the NSERC Industrial Research Chair in Aviation Acoustics and the Green Aviation Research & Development Network (GARDN). The authors also thank Jacques Royer from Dickie Moore Rentals where the experimental setup was installed.

CRJ200 - A CRJ200 - B C

B w

/o A

CC

LE

AN

-PSF

w/o

AC

p[d

B]

p[d

B]

p[d

B]

p[d

B]

x [m] x [m]

x [m] x [m]

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REFERENCES

1. A. Malkoun and R. Lapointe. “Exterior acoustic array measurements on the Bombardier Global Express,” Canadian Acoustics, 35, 30–31 (2007).

2. D. H. Johnson and D. Dudgeon, Array signal processing: Concepts and techniques, Prentice Hall Signal Processing Series, Alan V. OppenHeim Series Editor, (1993).

3. P. Sijtsma, “Acoustic beamforming for the ranking of aircraft noise.” Tech. Rep. NLR-TP-2012-137, National Aerospace Laboratory NLR (2012).

4. T. F. Brooks and W. M. Humphreys, “A deconvolution approach of the mapping of acoustic sources (DAMAS) determined from phased microphone arrays,” J. Sound Vib., 294, 856-879 (2006).

5. E. Sarradj, “A fast signal subspace approach for the determination of absolute levels from phased micorphone array measurements,” J. Sound Vibration, 329, 1553-1569 (2010)

6. J. F. Piet, U. Michel and P. Böhning, “Localization of the acoustic sources of the A340 with a large phased microphone array during flight tests”, AIAA 2002-2506, (2002).

7. U. Michel, B. Barsikow, J. Helbig, M. Hellmig, and M. Schüttpelz, “Flyover noise mesurements on landing aircraft with a microphone array”, AIAA 1998-2336, (1998).

8. V. Fleury and J. Bulté, “Extension of deconvolution algorithms for the mapping of moving acoustic sources”, J. Acoust. Soc. Am. 129, 1417-1428 (2011).

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