Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model in predicting noise levels from departures around Schiphol airport is evaluated by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, and for maximum noise levels. The results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby demonstrating the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications that ultimately lead to a better quality of life for communities affected by aircraft noise.@en

In recent years, the search for more sustainable propulsion systems for aviation has led to an increased interest in electric propulsion aircraft. These aircraft are often claimed to be more silent due to the lack of an internal combustion engine which eliminates the combustion and exhaust noise. However, the dominant noise source for single-propeller aircraft, independent of the propulsion method, is often the propeller itself. Additionally, lower noise levels simply assessed with conventional sound metrics like the sound pressure levels might not necessarily correspond to lower noise annoyance. Therefore, this research investigates the noise levels and expected psychoacoustic annoyance of the electric air- craft Pipistrel Velis in comparison to its fuel-burning counterpart, the Pipistrel Virus. The Velis can serve as a direct replacement for the Virus as they have the same number of seats and the same take-off weight, raising the question of what the effect of this replacement is on community noise annoyance. The research is based on experimental measurements during flyovers of both aircraft. Overall, it was found that the Velis is found to have lower noise emissions and lower psychoacoustic annoyance ac- cording to its sound quality metrics. This is reflected in the loudness, tonality, and impulsiveness, likely due to the presence of fewer tones below 500 Hz.@en

Advanced Air Mobility (AAM) vehicles are usually capable to operate in both forward and vertical flight thanks to their design configurations featuring multiple (tilting) rotors. Such maneuvering agility can be leveraged to minimize their acoustic footprint during operations close to the ground. The present work compares the acoustic footprint of optimal trajectories of AAM aircraft using low-order aero-acoustic models. The methodology is applied to the case of an AAM quad-rotor aircraft, which is modelled as a rotating point-mass with three Degrees of Freedom and two input controls. The trajectories are globally optimal in the sense of standard mission objectives, such as maneuver time or traveled horizontal distance, and are subject to realistic performance constraints. Results show that minimum-time trajectories generate higher and more concentrated noise footprints compared to minimum-distance trajectories, which distribute noise levels more evenly and result in overall lower noise footprints. Departure trajectories exhibit lower noise levels than approach trajectories. Adopting minimum-distance trajectories can significantly reduce noise impacts for both approach and departure maneuvers. @en

This manuscript presents a psychoacoustic analysis of the noise emissions from the Airbus A320 aircraft family, with a special focus on the tonal noise emitted by its nose landing gear (NLG) system. The study is based on microphone array measurements of aircraft flyovers under operational conditions performed next to Amsterdam Airport Schiphol. It was found that the NLG system is a dominant tonal noise source for all aircraft subtypes measured (A319, A320, A320neo, A321, and A321neo) around 1700 Hz. The magnitude of the tonal noise observed was strongly correlated with the aircraft velocity, whereas the tonal frequency remained relatively constant. A preliminary psychoacoustic analysis between the different aircraft subtypes showed that, on average, the neo versions presented higher metric values for effective perceived noise level (EPNL), psychoacoustic annoyance, and loudness but lower tonality than their older ceo counterparts. However, given the low number of neo samples available in this study, no strong conclusion can be drawn from this analysis. Overall, the A321 aircraft measured presented lower average values for all noise metrics evaluated than the A320 ones, despite being larger and heavier. These claims will be evaluated in upcoming dedicated psychoacoustic listening experiments.

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The growth shown by the aviation industry has given significant economic benefits, but also causes disturbance to communities living near airports, including annoyance and potential health problems due to the high aviation-induced noise levels. Therefore, regulations are implemented by imposing limits on the yearly cumulative noise levels at specific locations around airports. This requires understanding and prediction of the varying aircraft noise levels. This is traditionally achieved by using so-called best-practice or regulatory models, such as the Dutch aircraft noise model, which require low computational costs and limited model inputs. This way of noise monitoring comes with significant model approximations and hence potential deviations of the model predictions from the actual noise levels. The limitations of this current approach has given rise to distrust in communities near airports. Hence, the models need to be validated against real measurements, for which use is made of the stations from the Noise Monitoring System (NOMOS) around Schiphol Airport. In this contribution, we analyzed the time series of the yearly averaged L_den measured at 35 NOMOS stations for the period from 2006 to 2022. A distinction is made between noise from aircraft and that due to other noise sources. We observe a decreasing trend of 0.52 ± 0.04 dB(A)/year in L_den for the investigated time period. One of the objectives is to determine how the measured L_den can be assigned to the various aircraft types in the fleet at Schiphol. This is performed by an L_den time series analysis of the averaged time series over all stations, combined with changes in the fleet composition. The results from the unconstrained least squares (LS) and non-negative least squares (NNLS) methods are presented and compared. Based on the obtained model for 2006-2020, predictions are performed for 2021 and 2022.

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To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. In this research the Noise-Power-Distance (NPD) tables in the European Doc.29 noise model are evaluated with measurements around Amsterdam Airport Schiphol. Even with accurate input parameters (thrust and distance to the observer), differences in modelled and measured noise levels are found, which are assumed to be due to the errors in the NPD table. To further investigate this, thrust-noise relations are derived from measurements. These relations are found to differ from the original NPD tables. Using the measured thrust-noise relations, the modelled and measured mean noise levels are in agreement and the standard deviation of the differences is reduced by 25% for departure operations. This finding is consequently verified with independent measurements around Oslo Airport Gardermoen. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD tables.@en

This paper investigates the prediction accuracy and time efficiency of two distinct low-order methods, Opty∂B and LOPNOR , for predicting tonal and broadband noise of a drone rotor in axial and non-axial flow conditions. These are both derived from an aero- dynamic rotor model based on the blade element momentum theory, respectively coupled with a time- (Opty∂B) and frequency-domain (LOPNOR) solution of the Ffowcs Williams- Hawkings (FW-H) integral equation applied to a radial distribution of acoustically compact and non-compact sources. Experimental data and scale-resolving lattice-Boltzmann/very- large eddy simulation (LB/VLES) results for a two-bladed small unmanned aerial system (sUAS) in transitional boundary layer conditions are used to validate the low-order ap- proaches. Comparison between low-order, high-fidelity and experimental results reveal that the underlying sound generation mechanisms are accurately modelled by the low-fidelity methods, which therefore constitute a valid tool for preliminary design of quiet drone rotors or to estimate the noise impact of drone operations.@en

Correction Notice • Place s5 in front of the symbols ‘greater than or equal to (≥)’ and ‘less than (<)’ in Equation 2, as shown below. (Formula Presented). • Replace the correlation coefficient ‘0.886’ showed in Table 6 to ‘-0.886’. This correction also apply to the paragraph below Section 3 (Correlation between Annoyance and Drone Characteristics) and in the last paragraph of the Conclusions. This negative value indicates an inverse relationship between the installation ratio (d/D) and the annoyance computed using the More PA model. • Replace ‘0.337 (Zwicker PA model)’ by ‘0.362 (Willemsen PA model)’ in the paragraph below Section 3 (Correlation between Annoyance and Drone Characteristics). This value (0.362) is also reported in Table 6.

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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.@en

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.@en

This paper introduces a methodology for predicting the noise footprint of urban air mobility (UAM) vehicles in vertiport environments during approach and departure maneuvers. The methodology integrates a flight mechanics model, an aerodynamic model, aeroacoustic models, and a noise propagation model. The flight mechanics model employs a point-mass dynamic model to determine optimal trajectories based on prescribed criteria. The aerodynamic model utilizes blade element momentum theory, while aeroacoustic models include frequency-domain acoustic formulation and a noise propagation model based on Gaussian beam propagation method, which accounts for 3D variations in terrain and atmospheric profiles. Noise footprints are computed for several waypoints, featuring significant variations in vehicle flight speed and pitch angle, and are subsequently compared. It is observed that variations in vehicle pitch angle significantly influence noise radiation directivity. Specifically, when the vehicle pitches up, on-ground noise levels beneath the source increase, while those at receivers farther away decrease. Conversely, when the vehicle pitches down, on-ground noise levels beneath the source decrease, while those at receivers farther away increase. Additionally, as flight speed increases, on-ground noise levels rise accordingly regardless of whether the vehicle pitches up or down. This trend suggests that lower flight speeds during approach and departure maneuvers are desirable to reduce the noise footprint. Furthermore, it is noted that building blocks further shield incoming noise and decrease noise levels at receivers distributed behind them. These findings underscore the necessity of the proposed approach in evaluating the noise footprint of UAM flight trajectories in vertiport environments, providing valuable insights for early design stages.@en

This paper presents a low-order method for assessing tonal noise from full-electric propeller-driven aircraft during outdoor operations. A high-fidelity numerical simulation and several outdoor measurements were performed to validate the approach and identify the dominant noise source. Outdoor measurements involve constant-altitude level flight and three take-off flights. The low-order method focuses exclusively on blade and hub geometry, while the numerical simulation considers propeller blades and the spinner. Comparison of outdoor measurements, numerical simulations, and low-order model predictions reveals the propeller as the primary noise source for the specified aircraft configuration, with negligible interactions with the airframe. The study further demonstrates that during take-off flights noise levels at higher harmonics are more sensitive to variations in propeller disk angles of attack. These findings underscore the importance of addressing propeller noise in full-electric propeller-driven aircraft. Additionally, the paper emphasizes the practical application of the low-order approach for evaluating the aircraft's noise footprint during take-off flights, providing crucial insights for early design stages.@en

This study investigated the acoustic and psychoacoustic properties of five quadcopters drones during realistic flyover scenarios, utilizing a 64-microphone array for outdoor recordings. Acoustic analyses encompassed signal-to-noise ratio (SNR) values, time-frequency sound pressure levels, and noise spectra at overhead positions. An analysis based on A-weighted SNR revealed discernible drone noise despite background noise. Significant noise levels were observed up to 12 kHz. Harmonics of blade passage frequencies were evident, influencing noise spectra up to 1 kHz. Unlike traditional aircraft, drones' proximity to the ground limits the atmospheric absorption effects of high-frequency noise. A psychoacoustic analysis focused on sound quality metrics (SQMs) and annoyance assessment. SQMs exhibited consistent patterns across attributes, such as sharpness, tonality, roughness, and impulsiveness, with notable drone-specific perceptions. Different annoyance models indicated varying degrees of annoyance perception, with the Autel EVO II drone (lowest installation ratio, defined as the ratio between the drone diagonal size and the propeller diameter) perceived as the most annoying and the DJI Phantom 4 (heaviest) as the least one. Propeller positioning, represented by the parameter of installation ratio, correlated significantly with annoyance levels, suggesting an influence on both noise signature and psychoacoustic response. These findings highlight the importance of understanding the acoustic and psychoacoustic impact of drones, particularly in urban environments.@en

High diversity seabed habitats, such as shellfish aggregations, play a significant role in marine ecosystem sustainability but are susceptible to bottom disturbance induced by anthropogenic activities. Regular monitoring of these habitats with effective mapping methods is therefore essential. Multibeam echosounder (MBES) has been widely used in recent decades for seabed characterization due to its non-destructive manner and extensive spatial coverage compared to traditional methods like bottom sampling. Nevertheless, bottom sampling remains essential to link ground truth with acoustic seabed classification. Using seabed samples and MBES measurements, machine learning techniques are commonly employed to model their relationships and generate classification maps of an extended seabed. However, limited ground truth data, resulting from constraints in regulations, budget, or time, may impede the development of robust machine learning models. To address this challenge, we applied a semi-supervised machine learning method to classify seabed sediments of a blue mussel (Mytilus edulis) cultivation area in the Oosterschelde, the Netherlands. We utilized nine boxcore samples to generate pseudo-labels on MBES data. These pseudo-labels enlarged the training data size, facilitated the training of three comprehensive machine learning algorithms (Gradient Boosting, Random Forest, and Support Vector Machine), and helped to classify the study site into mussel and non-mussel areas. We found the geomorphological and backscatter-related features to be complementary for mussel culture detection. Our classification results were demonstrated effective through expert knowledge of this cultivation area and brought insights for future research on natural mussel habitats.

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Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model–Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction (Formula presented.) % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions.

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This paper puts forward a novel design for an airspeed instrument aimed at small fixed-wing tail-sitter unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer present over the vehicle’s body in flight to its airspeed. The instrument consists of two microphones; one flush-mounted on the vehicle’s nose cone, which captures the pseudo-sound caused by the turbulent boundary layer, and a micro-controller that processes the signals and computes the airspeed. A feed-forward single-layer neural network is used to predict the airspeed based on the power spectra of the microphones’ signals. The neural network is trained using data obtained from wind tunnel and flight experiments. Several neural networks were trained and validated using only flight data, with the best one achieving a mean approximation error of 0.043 m/s and having a standard deviation of 1.039 m/s. The angle of attack has a significant impact on the measurement, but if the angle of attack is known, the airspeed could still be successfully predicted for a wide range of angles of attack.
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Aeroacoustic tests in closed wind tunnels are affected by reflections in the tunnel circuit and background noise. Reflections can be mitigated by lining the tunnel circuit. The present study investigates if lining exclusively the most accessible segment of a closed wind tunnel circuit, in particular the test section, is an approach which improves acoustic measurements. Literature shows that a wind tunnel lining material should have high acoustic absorption, low inertial resistivity and low surface roughness. Therefore, the test section of TU Delft's closed Low Turbulence Tunnel is lined with melamine foam wall liners. A total of 4 test section configurations were tested: baseline; test section with lining on the floor and ceiling; test section with lined side--panels; and test section lined at all surfaces (floor, ceiling and side--panels). An omnidirectional speaker is used for evaluating the wind tunnel's acoustic performance. A geometric modelling algorithm, based on the mirror-source method, is used to predict the effect of lining on primary reflections in the test section. In addition, reflections in the test section and in the tunnel circuit are characterized experimentally. The results show that the closed loop of the tunnel circuit is responsible for a long reverberation time in the test section. However, reflections inside the test section itself are the dominant source of acoustic interference at the microphone array location. The low fidelity geometric modelling algorithm is shown to be a valuable approach for an initial estimation of the acoustic benefit of lining, for both flow--off and --on conditions. Lining of the test section walls significantly reduces reflections from the reference source, as well as the aerodynamic background noise that reaches the array.@en

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results.@en

Threats posed by drones urge defence sectors worldwide to develop drone detection systems. Visible-light and infrared cameras complement other sensors in detecting and identifying drones. Application of Convolutional Neural Networks (CNNs), such as the You Only Look Once (YOLO) algorithm, are known to help detect drones in video footage captured by the cameras quickly, and to robustly differentiate drones from other flying objects such as birds, thus avoiding false positives. However, using still video frames for training the CNN may lead to low drone-background contrast when it is flying in front of clutter, and omission of useful temporal data such as the flight trajectory. This deteriorates the drone detection performance, especially when the distance to the target increases. This work proposes to pre-process the video frames using a Bio-Inspired Vision (BIV) model of insects, and to concatenate the pre-processed video frame with the still frame as input for the CNN. The BIV model uses information from preceding frames to enhance the moving target-to-background contrast and embody the target’s recent trajectory in the input frames. An open benchmark dataset containing infrared videos of small drones (< 25 kg) and other flying objects is used to train and test the proposed methodology. Results show that, at a high sensor-to-target distance, the YOLO algorithms trained on BIV-processed frames and concatenation of the BIV-processed frames with still frames increase the Average Precision (AP) to 0.92 and 0.88, respectively, compared to 0.83 when it is trained on still frames alone.

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