Calculation of the fan rotational speed based on flyover recordings for
improving aircraft noise prediction models
Roberto MERINO–MARTINEZ(1) ; Mirjam SNELLEN(1) ; Dick G. SIMONS(3)(1) Delft University of Technology, the Netherlands, r.merinomartinez@tudelft.nl

Abstract
The enforcement of noise control regulations around airports usually depends on the estimations of aircraft
noise prediction models. Current best–practice noise contour calculation methods assume default engine thrust
values depending on the engine type and the altitude of the aircraft. These prediction tools provide a single
noise level for a certain aircraft type in a certain flight phase and at a specific distance from the observer. In
practice, however, changes in the thrust occur and cause variations in the noise levels of several decibels. In thispaper, an approach is presented to estimate the fan rotational speed N1% (and hence the thrust) directly fromflyover audio recordings. This method estimates the blade passing frequency (BPF) of the fan by searching its
characteristic tonal peak (and its higher harmonics) and accounting for the Doppler effect. This method wasapplied to more than 400 measurements of Airbus A330–300 aircraft. The results show a significant correlation
between the recorded noise levels and the fan rotational speed, explaining up to 43% of the variability in noiselevels. Considering the calculated N1% values in the noise prediction models notably increases the agreementof the estimations with the recorded noise levels.
Keywords: Aircraft noise, Turbofan noise, Noise prediction models

1

INTRODUCTION

Due to the increasingly importance of aircraft noise because of the continuous growth in air traffic, obtaining
precise information about the aircraft noise levels is essential for properly enforcing environmental laws aroundairports, route management and and land–use planning [1, 2].
The typical approach for estimating aircraft noise levels is based on legal compliance methods, such as thosedescribed in Document 29 of the ECAC (European Civil Aviation Conference) [3], which allow for the calculation of noise contours around airports to estimate the average noise exposure of aircraft operations over a
relatively long period of time and set noise limits. This type of methodology can be called best practice since
relatively large assumptions are made, either for practical reasons or due to the lack of detailed data. The noiselevels are estimated for each operation using look–up tables, such as the noise–power–distance (NPD) tables,
whereby the aircraft’s thrust settings and the distance to the observer are the main inputs. Previous comparisons between aircraft noise prediction models and experimental measurements [4–7] showed that differencesof several decibels exist between modeled and measured noise levels, which can lead to important errors whenassessing the annoyance experienced by the population living around airports. For example, a mere differenceof 1 dB in the predictions can lead to changes of about 20% in the noise contours [8]. Therefore, experimental
validations and potential improvements of these models are of great interest [2].
Other more sophisticated, semi–empirical aircraft noise prediction methods, such as ANOPP [9] or PANAM [10]model the sound emission and propagation separately and provide more accurate predictions, but they requirevery detailed input data, which are not typically publicly available [5]. In addition, these programs are normallynot accessible to other users and are limited to a small number of aircraft types. Therefore, this paper focuseson the best practice method of ECAC’s Doc. 29. For a more detailed explanation on how to use this method,
the reader is referred to [2, 3].
These simpler best practice prediction tools, such as the NPD tables, provide a single noise level for a certain
aircraft type, in a certain flight phase and at a specific distance from the observer [11, 12]. Previous studies

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[13–15], however, showed that changes in certain aircraft settings (especially the fan rotational speed) produce
variations in the noise levels of several decibels. Not accounting for this fact can considerably hamper anaccurate calculation of noise contours. The noise prediction models use relatively simple estimations (the so–called thrust profile) for the net engine thrust for each flight phase.
In order to evaluate the current assumptions regarding net thrust made by the noise contour models, the actual
engine fan settings N1% (i.e., the relative fan rotational speed) and the distance between the aircraft and theobserver need to be determined. The term engine fan setting [4, 13–15] refers to the ratio between the fanrotational speed (in rpm), n, and the maximum fan rotational speed, nmax , i.e., N1% = 100 n/nmax because it
refers to the low–pressure shaft of the engine, on which the fan is mounted. Even though the actual aircraft
settings are recorded by airlines, these data are not publicly disclosed for reasons such as pilot privacy andto ensure the operation business strategies of airlines. To compensate for this lack of information, a method
to determine N1% based solely on audio files is proposed and described in this paper. Whereas some of theaforementioned studies [4, 7, 13–18] made use of phased microphone arrays to analyze the noise contributionsof different aircraft components, the present research only studies the total aircraft noise levels recorded by anindividual microphone from the Noise Monitoring System (NOMOS) around Amsterdam Airport Schiphol in
the Netherlands [2].
Thus, the aim of this paper is to investigate the effects of considering the instantaneous thrust setting on thenoise level predictions, rather than employing tabulated values as in practice. A large set of experimental
data, containing more than 400 flyovers of Airbus A330–300 (henceforth Airbus 333) aircraft under different
operational modes (arrivals and departures), was considered to compare the N1% values used by the models
with those found experimentally. A correlation analysis was performed between the recorded noise levels andthe measured engine fan settings to assess the importance of this parameter in the noise emissions. Lastly, acomparison between the noise levels predicted using the NPD tables and those recorded experimentally is alsopresented.

2

EXPERIMENTAL SETUP

The study is based on 433 audio files recorded by one noise monitoring terminal (NMT 14) in the surroundingsof Amsterdam Airport Schiphol in 2016. The flyovers correspond to approaches (221) and departures (212)of Airbus 333 aircraft, which is a wide–body, twin–engine jet airliner designed for medium– to long–range
operations. Almost all the aircraft considered were equipped with engines from the CF6–80E1A family, which
are the ones considered for this study. The extension to more aircraft types and measurement locations can befound in [2].
The NOMOS system continuously measures the noise in residential areas around Amsterdam Airport Schipholusing calibrated Brüel & Kjær microphones. The microphones start recording whenever a certain thresholdsound pressure level (Lp ) is exceeded. The measurement point is located at Hoofdweg 1730, Abbenes in the
Netherlands (latitude 52.2353764 ◦ , longitude 4.5937646 ◦ and altitude -3 m).
Unfortunately, to reduce data storage, the NOMOS audio files are filtered and resampled with a sampling frequency of 8 kHz when stored, which causes a loss of all the spectral information for frequencies higher than
3500 Hz [2]. Hence, the noise metrics recorded by NOMOS (before the data compression) were used as areference. The maximum Overall A–weighted Sound Pressure Level (Lp,A,max ) was employed in this study but
the same conclusions apply to other metrics such as the sound exposure level (SEL or Lp,A,e ) or the effective
perceived noise level (EPNL) [2].
The trajectories of the aircraft flyovers were determined using ground radar data from air traffic control, whichis recorded every 4 seconds and interpolated linearly in between [2]. The average minimum distance to theobserver (r̄min ) for approaches (640 m) was considerably shorter than for departures (1110 m). The variability
in the minimum distances rmin is also considerably smaller for approaches (70 m) than for departures (about 730m) because all aircraft follow the instrument landing system approach. In order to have a more fair comparisonin the noise levels, these differences in distances to the observer were partially accounted for using a simple

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correction factor ∆Lp (considering approaches and departures separately) for the sound spreading which wasadded to the recorded Lp,A,max valuesrmin∆Lp = 20 log
r̄min


.

(1)

This correction factor is positive for minimum distances to the observer larger than the average (rmin > r̄min )and vice versa, i.e., the noise levels of aircraft flying further away than r̄min are increased and vice versa.
The weather conditions showed similar values along the duration of the measurements. The temperatures wereused to calculate the sound speed and certain parameters required for the net thrust estimations performed by
the NPD tables, and the wind velocities for calculating the true airspeeds of the aircraft together with the groundradar data [2]. The velocities presented similar values (about 70 m/s) for both approaches and departures at the
moment when the distance was minimum to the observer. The recorded flyovers had a variability in velocity
for both operation types of about 20 m/s.

3

ESTIMATION OF AIRCRAFT ENGINE SETTINGS

The rotation of the turbofan produces tonal sound because of the interaction between the fan blades and thestator vanes. The fundamental frequency of this sound, f1 , is called blade passing frequency (BPF) and depends
on the fan rotational speed n:
BPF = f1 =

Bn
,
60

(2)

where B is the number of fan blades (38 in this case).
Higher harmonics of the BPF are usually found as well in the sound signature of aircraft flyovers. The frequencies of these harmonics, fk , are multiples of the BPF ( fk = k f1 , k = 1, 2, 3 . . . ). Unfortunately, due to therelatively low maximum frequency available in this study (3500 Hz), few higher harmonics (up to the fifth)were found in the spectrograms in practice.
The Doppler effect due to the relative motion of the aircraft with respect to the observer needs to be accounted
for in the fan rotational speed estimation process. The expression for the Doppler–shift ( f 0 / f ) between theobserved frequency f 0 and the emitted frequency f isf1=,
M k cos (θ )f0
1 − kM(3)

where k·k is the Euclidean norm of the vector, M is the Mach number vector, M = V /c, V is the source
velocity vector, c is the speed of sound and θ is the angle between the position vector of the source withrespect to the observer, r , and V .
The variation of the polar emission angle θ and the Doppler shift for an example flyover recording are presented
in Fig. 1a. The Doppler effect can be observed in the varying frequency of the engine fan tones in the
spectrograms depicted in Figs. 1b and 1c as black curves.
Thus, the first step in this analysis is to determine the BPF value for each flyover audio recording and thencalculate the fan rotational speed n using Eq. (2). The inputs necessary for this method are the trajectoryand engine characteristics of the aircraft and the audio recording of the flyover. The expected continuous andmonotonically decreasing Doppler shift can be estimated using the aircraft trajectory and Eq. (3), see Fig. 1a.
Afterwards, a least–squares curve–fitting process is performed to ensure that the measured narrow–band engine
fan tones in the signal’s spectrogram agree with the predicted Doppler shift, see thick black line in Fig. 1b.
The spectrograms are calculated by using 2048 samples per time block with Hanning windowing and a 50%
data overlap. For each time step, peaks over a certain threshold are considered as candidates for the BPF.
Afterwards, the characteristic Doppler–shifted curves corresponding to the fan tonal noise are searched for by

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180

1.5

135

1.25

90

1

45

Doppler shift, [deg]using these peaks. First, the BPF is searched for in the spectrogram within a predefined frequency band overtime, depending on the operation mode and engine characteristics. Changes in the engine fan settings performedby the pilot during the recording time or due to the turbulence in the atmosphere can also cause “bumps” in the
curves representing the engine fan tones see Fig. 1c. These irregularities can be accounted for by allowing asmall increase (or decrease) in the Doppler–shifted tone frequency with respect to time. The allowable increaseor decrease is defined by the user. The presence of higher harmonics is also evaluated by searching for multiplesof the estimated BPF and serves as a further confirmation that the obtained BPF value is correct, see Fig. 1cwhere four additional harmonics are clearly detected. This process provides a (Doppler–corrected) BPF value(for the instant when r = rmin ) and, therefore, an N1% value. The obtained N1% needs to be checked to confirm
that it falls within the typical range depending on the aircraft operation (approach or departure). In case theprovided N1% value is not realistic, the whole process is repeated using a different search frequency band for
the BPF, i.e., a different initial value for the BPF.

0.75
Doppler shift

0

0

4

8

12

16

20

24

0.5
28

Time, [s]

(a)

(b)

(c)

Figure 1. (a) Angle θ and Doppler shift during an example recording. Spectrograms of the audio signal with:(b) Curve fitted to the Doppler shift. (c) Detected engine tones. The vertical solid black line represents the timewith r = rmin .

4

RESULTS

4.1 Comparison between measured and modeled engine fan settings
Figure 2a shows that the fan rotational speeds are higher during departure (N1 = 90%) than during approach(N1 = 70%), as expected. However, larger variabilities are found in N1% during approach, with differences ofabout 30%, whereas during departure these differences are roughly 10%, which indicates that pilots need toadjust the throttle to keep the aircraft on the desired flight path during approach, but for departures the throttle
is more fixed. The predicted N1% values using ECAC’s Doc. 29 approach [2, 3] for these cases are plotted as
black dots on the right of each box plot. For approach, this value falls within the 25th and the 75th percentiles(i.e., the box) of the experimental values, although below the median of these. For departures, on the other
hand, the N1% value considerd by the NPD tables is slightly higher.
The box plot in Fig. 2b presents the variations in the Lp,A,max metric provided by NOMOS and corrected forthe distance following Eq. (1). The median values for both cases are comparable and between 65 and 70 dBA.
However, it should be kept in mind that the distance correction of Eq. (1) is applied to each operation caseindividually, meaning that the higher value of r̄min for departures is not accounted for. That is why departures
have comparable Lp,A,max values as approaches. The predicted noise levels by the NPD tables for the selectedflyovers are plotted as black lines on the right of the box plots, showing a satisfactory agreement for approaches
but a considerable overestimation for departures. In general, variabilities in Lp,A,max of about 20 dBA are
observed in the experimental data for both cases, whereas the NPD results show a spread of only 3 dB for
approach and 7 dB for departure.

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90

100

Lp,A,max , [dBA]N1 at r = rmin , [%]

90
80
70

80

70

60

60
50
Approach50

Departure

Approach(a)Departure(b)

Figure 2. Box plots indicating the variability of the: (a) Calculated N1% at r = rmin . (b) Measured Lp,A,max . The
red crosses represent the outliers and the black dots and lines on the right of the boxes denote the estimationsof the NPD tables.
4.2 Correlation analysis of noise level variations
The results of the correlation analysis between the recorded Lp,A,max and the calculated engine fan settings(N1%) at r = rmin are presented in Figs. 3a and 4a. The correlation coefficient ρ, the coefficient of determination ρ 2 and the p–value are included in the legend of each graph. A common criterion to consider a correlation
significant is that the p–value should be lower than 0.05 [15]. Following this criterion, the two correlations observed are deemed as significant. A coefficient of determination between N1% and Lp,A,max of around 0.3 wasfound for the approaches. Similar values were obtained by Snellen et. al. [14] for landings of Boeing 737 andFokker 70 aircraft. A higher value for ρ 2 (around 0.43) was found for the departure operations in Fig. 4a. Thisis expected because engine noise is supposed to be more dominant during departure than during approach [16],so a stronger correlation with the engine fan settings is justified.
No significant correlation was found between the recorded Lp,A,max and the aircraft true airspeed at r = rmin [2].
Approaches NPD

85

Approaches= 0.08817= 0.00777
p-value = 3.34191e-01
Least Squares fit

80

80

70

75
70
65
60
55

60

50
50

60

70

N1 at r = rmin , [%]

(a)

80

90

45
45

Approaches - New predictions

80

2

Predicted L p,A,max, [dBA]

NPD L p,A,max, [dBA]

Lp,A,max , [dBA]90= 0.53939
2= 0.29094
p-value = 1.45296e-10
Least Squares Fit

85

75
70
65
60
= 0.47777= 0.22826
p-value = 2.62473e-08
Least Squares fit

55

2

50
50

55

60

65

70

75

Measured L p,A,max, [dBA]

(b)

80

85

45
45

50

55

60

65

70

75

80

85

Measured L p,A,max, [dBA](c)Figure 3. (a) Correlation analysis between Lp,A,max and N1% at r = rmin for approaches. Recorded Lp,A,max vs.
modeled Lp,A,max using (b) default or (c) experimental N1% values for approaches.

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Departures NPD

85

Departures

80

70

75
70
65
60
= 0.08817
2= 0.00777
p-value = 3.34191e-01
Least Squares fit

55

60

50
70

75

80

85

90

N1 at r = rmin , [%]

95

100

45
45

Departures - New predictions

80

Predicted L p,A,max, [dBA]80= 0.65796
2= 0.43292
p-value = 5.72675e-15
Least Squares Fit

NPD L p,A,max, [dBA]

Lp,A,max , [dBA]

90

85

50

55

60

65

70

70
65
60
= 0.47777= 0.22826
p-value = 2.62473e-08
Least Squares fit

55

2

50
75

80

85

Measured L p,A,max, [dBA]

(a)

75

(b)

45
45

50

55

60

65

70

75

80

85

Measured L p,A,max, [dBA](c)Figure 4. (a) Correlation analysis between Lp,A,max and N1% at r = rmin for departures. Recorded Lp,A,max vs.
modeled Lp,A,max using (b) default or (c) experimental N1% values for departures.
Table 1. Average error (ε), average of the absolute values of the errors (εabs ), and standard deviation (σ ) for
Lp,A,max predictions with the NPD tables’ default estimations of N1% (named as NPD) and using the N1%
values obtained experimentally (named as Exp). All the values are in dBA.
ParameterεNPD
εExpεNPD, abs
εExp, abs
σNPD
σExp

Approach
1.65
-0.64
2.65
2.18
3.58
3.07

Departure
-2.97
0.46
5.38
3.78
6.05
5.66

4.3 Comparison of recorded noise levels with modeled ones
In order to assess the importance of including the more accurate estimations of the N1% values obtained withthe method introduced in this paper compared to the default ones used in the NPD tables, a comparison withthe recorded NOMOS data was made twice: 1) considering the default N1% estimations by the NPD tables and2) using the N1% values found experimentally. It should be noted that the distance correction of Eq. (1) was
not applied in the Lp,A,max results shown in this section, since the NPD tables already account for the effects ofthe distance to the aircraft, r.
The results of both comparisons are gathered in Figs. 3 (b and c) and 4 (b and c) for approaches and departures,
respectively. The diagonal black dashed line in Figs. 3 and 4 represents a perfect agreement between estimatedand measured Lp,A,max levels (ρ = 1). The least squares fit for each case is also plotted. Considering theexperimental N1% values produces a better agreement between the modeled and measured values of Lp,A,max
and a five–fold increase of the correlation coefficient.
The average error (ε) and the average of the absolute values of the errors (εabs ) made by both predictions(using default or calculated N1% values) for each case are presented in Table 1. The error is defined as thedifference between the measured and the estimated Lp,A,max . Hence, ε > 0 means that the model underpredicts
the actual noise levels and vice versa. The standard deviations of these errors, σ , are also included in Table 1.
On average, including the experimental estimations of N1% reduces ε by 1 dBA and εabs and σ by 0.5 dBA
each. Despite this improvement, there is still a significant discrepancy between the noise measurements and thenoise model predictions. Additionally, it can be observed that in all the graphs there are several outliers that1605

are severely underpredicted by both NPD predictions by up to 15 dBA.
It should be noted that in this research, the experimental values of the aircraft velocity or altitude have beenused for the NPD calculations, instead of using default tabulated ones, as usual. Therefore, the actual NPD estimations for Lp,A,max are most probably even less accurate than those presented here. Hence, the error reduction
obtained by using more accurate input values could be even higher in reality. The approach of including theactual aircraft parameters was taken to isolate the influence of the choice of N1% for NPD predictions.

5

CONCLUSIONSAn automatic approach to determine the engine fan settings (N1%) directly from flyover audio recordings was
proposed in this paper, in order to consider more precise values of this parameter as input for prediction models,rather than tabulated default values. This method was employed on a large data set of more than 400 Airbus
A330–300 aircraft flyovers recorded by the NOMOS system around Amsterdam Airport Schiphol.
A large variability was observed in the values of N1% obtained experimentally, compared to the default values
assumed by the noise models. Significant correlations were found between the calculated N1% values and themeasured noise levels. Introducing the N1% values obtained experimentally in the predictions reduced the errors
made with respect to the noise level recordings. However, there is still a remaining error and a large variability
in the noise levels unexplained by the noise prediction models.
After noticing the limitations of the noise prediction model employed in this paper, it is recommended to
improve such models, especially by employing more accurate individual flight parameters or accounting for thestrong influence of N1% in the noise levels. This recommendation is especially valid for more sophisticatedpurposes, such as noise abatement studies, rather than studies on the average exposure noise levels over longtime periods, where errors in the model estimates can balance out.
Additional research with other aircraft types is encouraged, especially with recordings containing a larger frequency range (i.e., no undersampled as here). Moreover, repeating this study using other noise metrics, such as
EPNL or new sound quality metrics [6, 19], is of high interest to better estimate the psychoacoustic annoyancearound airports.

ACKNOWLEDGEMENTS
The authors would like to acknowledge the support received by Sander J. Heblij and Dick H. T. Bergmans
throughout this project. They also want to thank Michael Arntzen from Amsterdam Airport Schiphol for providing the experimental data and critical thinking preparing this paper. The authors also acknowledge Henk
Veerbeek from the Netherlands Aerospace Centre (NLR) for providing data about the engines.

REFERENCES[1] Merino-Martinez R. Microphone arrays for imaging of aerospace noise sources. Delft University of Technology; 2018. ISBN: 978–94–028–1301–2.
[2] Merino-Martinez R, Heblij SJ, Bergmans DHT, Snellen M, Simons DG. Improving Aircraft Noise Predictions by Considering the Fan Rotational Speed. Journal of Aircraft. 2019;56(1):284–294.
[3] ECAC.CEAC Doc. 29. Report on Standard Method of Computing Noise Contours around Civil Airports.
Volume 2: Technical Guide. Neuilly sur Seine Cedex, France: European Civil Aviation Conference(ECAC); 2016. 4th edition.
[4] Merino-Martinez R, Bertsch L, Snellen M, Simons DG. Analysis of landing gear noise during approach.
In: 22nd AIAA/CEAS Aeroacoustics Conference. May 30 – June 1 2016. Lyon, France; 2016. AIAA paper
2016–2769.

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[5] Zellmann C, Schäffer B, Wunderli JM, Isermann U, Paschereit CO. Aircraft Noise Emission Model Accounting for Aircraft Flight Parameters. Journal of Aircraft. 2017 March–April;55(2):682–695.
[6] Merino-Martinez R, Vieira A, Snellen M, Simons DG. Sound quality metrics applied to aircraft components under operational conditions using a microphone array. In: 25th AIAA/CEAS Aeroacoustics Conference. May 20 – 24 2019. Delft, The Netherlands; 2019. AIAA paper 2019–2513.
[7] Merino-Martinez R, Neri E, Snellen M, Kennedy J, Simons DG, Bennett GJ. Analysis of nose landing gear
noise comparing numerical computations, prediction models and flyover and wind–tunnel measurements.
In: 24th AIAA/CEAS Aeroacoustics Conference. June 25 – 29 2018. Atlanta, Georgia, USA; 2018. AIAA
paper 2018–3299.
[8] Smith BS, Camargo HE, Burdisso RA, Devenport WJ. Development of a Novel Acoustic Wind Tunnel
Concept. In: 11th AIAA/CEAS Aeroacoustics Conference. May 23 – 25 2005. Monterey, California, USA;2005. AIAA paper 2005–3053.
[9] Kontos KB, Janardan BA, Gliebe PR. Improved NASA–ANOPP Noise Prediction Computer Code forAdvanced Subsonic Propulsion Systems. Volume 1: ANOPP Evaluation and Fan Noise Model. NASA
CR–195480; 1996. NASA CR–195480.
[10] Bertsch L, Dobrzynski W, Guérin S. Tool Development for Low–Noise Aircraft Design. Journal of Aircraft.
2010 March–April;47(2):694–699.
[11] Procedure for the Calculation of Airplane Noise in the Vicinity of Airports, Aerospace Information Report
AIR 1845. 400 Commonwealth Drive, Warrendale, PA 15096: Society of Automotive Engineers, Inc.
(SAE) - Committee on Aircraft Noise (SAE A-21); 1986. 1845.
[12] Recommended method for computing noise contours around airports (ICAO Circular 205–AN/1/25). Montreal, Canada.: International Civil Aviation Organization (ICAO); 1988. 9911.
[13] Snellen M, Merino-Martinez R, Simons DG. Assessment of aircraft noise sources variability using an
acoustic camera. In: 5th CEAS Air & Space Conference. Challenges in European Aerospace. September 7– 11 2015, Delft, Netherlands. Paper 2015–019. Council of European Aerospace Societies;. .
[14] Snellen M, Merino-Martinez R, Simons DG. Assessment of noise level variability on landing aircraft usinga phased microphone array. Journal of Aircraft. 2017;54(6):2173–2183.
[15] Merino-Martinez R, Snellen M, Simons DG. Determination of Aircraft Noise Variability Using an Acoustic
Camera. In: 23rd International Congress on Sound and Vibration, July 10 – 14 2016, Athens, Greece; 2016.
.
[16] Merino-Martinez R, Snellen M, Simons DG. Functional beamforming applied to imaging of flyover noiseon landing aircraft. Journal of Aircraft. 2016 November–December;53(6):1830–1843.
[17] Merino-Martinez R, Snellen M, Simons DG. Functional Beamforming Applied to Full Scale Landing
Aircraft. In: 6th Berlin Beamforming Conference, February 29 – March 1 2016, Berlin, Germany. GFaI,e.V., Berlin; 2016. BeBeC–2016–D12.
[18] Merino-Martinez R, Neri E, Snellen M, Kennedy J, Simons DG, Bennett GJ. Comparing flyover noise
measurements to full–scale nose landing gear wind–tunnel experiments for regional aircraft. In: 23rd
AIAA/CEAS Aeroacoustics Conference. June 5 – 9 2017. Denver, Colorado, USA; 2017. AIAA paper
2017–3006.
[19] Merino-Martinez R, Pieren R, Snellen M, Simons DG. Assessment of the sound quality of wind turbine
noise reduction measures. In: 26th International Congress on Sound and Vibration, July 7 – 11 2019,
Montreal, Canada; 2019. .

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