A.S. Jayanthi
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1
This paper proposes a measurement-based methodology for investigating the ground effect observed in real-life aircraft flyover measurements in low-noise regions. An acoustic array is used for obtaining a spectrogram of the sound with ground reflections to a large extent eliminated. By applying a model to account for the ground effect, a semi-synthetic spectrogram is obtained. This spectrogram is then qualitatively compared with the spectrogram obtained from single microphone measurements (Munisense) at two heights to verify whether indeed the semi-synthetic spectrogram agrees with those measured by corresponding results. As a next step, the ground effect for the flyovers measured is quantified by using the array-recorded signals together with the model for the ground effect for all flyovers. By correcting Munisense signals for the estimated ground effect, improved agreement between noise levels at both receiver heights and the array measurements is obtained. Noise differences are evaluated using the proposed alternative metric that only accounts for levels within 4 s duration. For flyovers with higher elevation angles, a greater reduction in noise, up to 2.3 dBA, is observed, despite the larger slant distances. In contrast, flyovers at lower elevation angles show a smaller reduction of approximately 1.8 dBA. This contrasts with the common corrections applied for lateral attenuation in best-practice modeling where the attenuation (accounting for more than only the ground effect) diminishes at higher elevation angles.
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This paper proposes a measurement-based methodology for investigating the ground effect observed in real-life aircraft flyover measurements in low-noise regions. An acoustic array is used for obtaining a spectrogram of the sound with ground reflections to a large extent eliminated. By applying a model to account for the ground effect, a semi-synthetic spectrogram is obtained. This spectrogram is then qualitatively compared with the spectrogram obtained from single microphone measurements (Munisense) at two heights to verify whether indeed the semi-synthetic spectrogram agrees with those measured by corresponding results. As a next step, the ground effect for the flyovers measured is quantified by using the array-recorded signals together with the model for the ground effect for all flyovers. By correcting Munisense signals for the estimated ground effect, improved agreement between noise levels at both receiver heights and the array measurements is obtained. Noise differences are evaluated using the proposed alternative metric that only accounts for levels within 4 s duration. For flyovers with higher elevation angles, a greater reduction in noise, up to 2.3 dBA, is observed, despite the larger slant distances. In contrast, flyovers at lower elevation angles show a smaller reduction of approximately 1.8 dBA. This contrasts with the common corrections applied for lateral attenuation in best-practice modeling where the attenuation (accounting for more than only the ground effect) diminishes at higher elevation angles.
This paper presents an approach for modeling the non-linear behavior of aircraft flyover noise using data recorded by a network of 41 Noise Monitoring Terminals (NMTs) surrounding Amsterdam Schiphol Airport. This approach leverages measurements of the acoustic metrics SEL and LA,max to train a regression-based Random Forest model, which incorporates a new feature for real-time wind direction correction, along with community noise levels. The model achieves a high predictive accuracy, with a standard deviation of residuals below 2 dBA for both SEL and LA,max. To enhance interpretability, both global and local feature importance analyses are employed, providing complementary insights into the influence of input features relative to each other. The resulting feature rankings for both SEL and LA,max are in line with physical expectations: apart from community noise levels, noise event duration and distance are identified as the most influential parameters for predicting SEL, while community noise levels and distance play dominant roles in predicting LA,max . These findings demonstrate that the proposed framework effectively captures the physical dependencies while maintaining interpretability and robustness.
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This paper presents an approach for modeling the non-linear behavior of aircraft flyover noise using data recorded by a network of 41 Noise Monitoring Terminals (NMTs) surrounding Amsterdam Schiphol Airport. This approach leverages measurements of the acoustic metrics SEL and LA,max to train a regression-based Random Forest model, which incorporates a new feature for real-time wind direction correction, along with community noise levels. The model achieves a high predictive accuracy, with a standard deviation of residuals below 2 dBA for both SEL and LA,max. To enhance interpretability, both global and local feature importance analyses are employed, providing complementary insights into the influence of input features relative to each other. The resulting feature rankings for both SEL and LA,max are in line with physical expectations: apart from community noise levels, noise event duration and distance are identified as the most influential parameters for predicting SEL, while community noise levels and distance play dominant roles in predicting LA,max . These findings demonstrate that the proposed framework effectively captures the physical dependencies while maintaining interpretability and robustness.
Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .
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Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .
Since 2020, all commercial aircraft have been mandated to be equipped with ADS-B Out
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated. ...
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated. ...
Since 2020, all commercial aircraft have been mandated to be equipped with ADS-B Out
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated.
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated.
Conference paper
(2024)
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Anandini Sravya Jayanthi, Rebekka van der Grift, Irene Dedoussi, Mirjam Snellen
To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where Lden = 45 - 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented. Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.
...
To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where Lden = 45 - 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented. Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.