Satellite-derived bathymetry (SDB) provides a cost-effective solution for coastal mapping, but challenges remain in model interpretability and uncertainty quantification. This study investigates the applicability of the least-squares-based deep learning (LSBDL) framework for SDB, leveraging its hybrid structure that integrates neural networks with the available least-squares theory to enhance model transparency. ICESat-2 photon-counting LiDAR was used to train depth estimation from Sentinel-2 multispectral imagery over an approximately 30 km × 30 km region of near-coastal bathymetry at Anegada, British Virgin Islands. ICESat-2 provided high-precision depth information, of which 80% were used for training and the remainder for validation. LBSDL depth estimation achieved a root-mean-square error (RMSE) of 2.74 m, representing around 10% of the maximum observed depth, with the best performance in the 2–15 m depth range. These findings demonstrate the potential of LSBDL for interpretable and reliable bathymetric mapping, highlighting ICESat-2 as a globally accessible training and validation source and advancing SDB capabilities for data-sparse coastal regions.

The multibeam echosounder (MBES) has been widely used in seabed mapping, considering its ability to collect continuous and broad-scale seabed measurements efficiently. The presence of shellfish or dead shell material can alter the geophysical properties of the sediment and thus affect the MBES backscatter intensity, making acoustic surveys with the MBES a potential non-invasive solution for regularly monitoring the benthic habitats of shellfish aggregations. Although there exists an increasing interest in mapping marine benthos with MBES measurements recently, the use of multi-spectral backscatter data is still limited. Thus, this research aims to enhance the acoustic mapping of benthic habitats using multi-spectral MBES data, with a focus on a shell bed region in the Dutch North Sea. With backscatter measurements from three frequencies, 90, 300, and 450 kHz, we achieved seabed classification in two steps. First, a semi-supervised backscatter completion was conducted to generate full-coverage backscatter data for each incident angle, mitigating the limited overlap between adjacent survey lines. We then classified the multi-Angle backscatter data from each individual frequency using the Gaussian Mixture Model. Our results indicate an improved seabed classification performance compared to the classical Bayesian method. Comparisons of classification maps across frequencies also show their different abilities to distinguish the shell bed region from other coarse sediments, demonstrating the value of leveraging multi-spectral backscatter data in seabed habitat mapping.

This study covers three aspects of acoustic localisation of drones using a microphone array. First, it assesses a grid-free approach, using differential evolution, to estimate the three-dimensional position of a drone. It is found that this is indeed possible for the drone in the near-field. For larger distances, it still provides the angular position of the drone. Second, the study emphasizes the essence of localisation over small frequency bands with the bands jointly spanning a large frequency range to reveal the presence of multiple sound sources and maximise the drone localisation range. Third, it addresses the localisation ranges for six different drones.

Many highly interdependent disciplines are concerned with the system noise assessment of small propeller aircraft concepts, including aircraft, propeller, engine design and acoustic modelling, flight trajectories calculation, noise propagation and ground effect modelling. There are no state-of-the-art simulation processes in Europe which account for all the aforementioned disciplines, as efforts heretofore have been focused on large transport aircraft concepts. This paper presents the development of such a simulation process at the German Aerospace Center (DLR). The simulation process inputs top-level aircraft design requirements, producing a valid CS-23 conceptual propeller aircraft design. It then calculates realistic flight paths for departure and approach for the conceptual aircraft and simulates noise immissions at user-specified locations. The immissions assessment would then guide design modifications, low-noise flight trajectory generation and novel aircraft design. A Reims-Cessna F406 is considered as the reference aircraft and is used to obtain the first results from the simulation process. The results are compared with measurements from a flight test campaign using the same aircraft. Important effects observed in the results and in the immissions assessments are also presented in this paper. Furthermore, the capabilities of the simulation chain will be demonstrated with sensitivity studies that show the effects of modifying operational procedures on ground noise immissions.

This paper investigates the prediction accuracy and time efficiency of two distinct low-fidelity methods for predicting the tonal and broadband noise of a drone rotor in axial and non-axial inflow conditions. These are both derived from an aerodynamic rotor model based on the blade element momentum theory, respectively coupled with a time- and a frequency-domain solution of the Ffowcs Williams-Hawkings 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 results for a two-bladed small unmanned aerial system in transitional boundary layer conditions are used to validate the low-fidelity approaches. Comparison between low-fidelity, high-fidelity and experimental results reveal that the underlying sound generation mechanisms are accurately modeled by the low fidelity methods, which therefore constitute a valid tool for the preliminary design of quiet drone rotors and for the estimation of the community noise impact of drone operations.

Sound propagation in closed test section wind tunnels suffers from reflections and diffraction, which compromise acoustic measurements. In this article, it is proved possible to improve the post-processing of phased-array microphone measurements by using an approach based on the combination of numerical acoustic simulations and beamforming. A Finite Element Method solver for the Helmholtz equation is used to model the acoustic response of the experimental facility. The simulations are compared with acoustic experiments performed at TU Delft's Low Turbulence Tunnel, using both fully reflective (baseline) and lined test sections. The solver accurately predicts the acoustic propagation from a monopole sound source at the centre of the test section to the microphones in the phased-array, for frequencies in the range 500Hz<f<2000Hz. It is shown that a (lower fidelity) geometric modelling method is unable to precisely predict the acoustic response of the Low Turbulence Tunnel at these frequencies, due to strong acoustic diffraction. The numerical results are used to implement corrections to the post-processing of experimental data. A corrected version of the Source Power Integration method is able to increase the accuracy of the source's noise levels calculation, based on a single numerical simulation with the source at the same location as in the experiment. A Green's function correction increases the beamforming resolution and the source's noise levels estimation accuracy from the beamforming maps, without a priori knowledge of the source's location. Both corrections perform well at processing flow-on acoustic measurements, and the Green's function correction shows an additional benefit. The improvement in beamforming spatial resolution leads to an increase of the signal to noise ratio.

For models that evaluate aircraft noise, thrust is an essential input. From aircraft flight recorder data or measured noise spectra, the engine's rotational speed can be estimated for which a conversion is then needed to obtain the engine's thrust. This research investigates three conversion methods. The first uses the expressions from the ANP database while the second method is based on the fuel flow. The third employs Gas Turbine Simulation Program (GSP) predictions. The thrust estimates are compared to airline performance calculations where significant variations up to 3 dBA in predicted noise were found. Methods one and three were found to be in good agreement with the performance data. An important finding of this paper is that combining methods one and three using least-squares is capable of providing the required conversion expressions, in line with those in the ANP database, but without being limited to a few aircraft types only.

Authorities are starting to pay attention not only to the noise levels of Unmanned Aerial Vehicles (UAVs) but also to their quality for acceptance. This manuscript presents a study of four types of propeller-driven UAVs (single-propeller quadcopter, coaxial-propeller quadcopter, quadplane eVTOL (electric vertical take off and landing) and tailsitter eVTOL) to assess their acoustic and psychoacoustic signatures. Experimental outdoor recordings are conducted under realistic flyover conditions. An acoustic analysis showed that quadcopters present higher noise levels compared to the eVTOLs, where the coaxial-propeller configuration revealed to be the noisiest and the quadplane the quietest. A psychoacoustic analysis demonstrated that the coaxial-propeller quadcopter was roughly three times more annoying than its single-propeller counterpart, whereas the quadplane and tailsitter eVTOLs showed similarly lower annoyance values. Additionally, the coaxial-propeller quadcopter exhibited the highest levels of loudness and impulsiveness, while the tailsitter had the lowest. Conversely, the tailsitter exhibited contrasting behavior in terms of sharpness. Regarding tonality, the quadplane was the most tonal, and the tailsitter eVTOL the least. In terms of modulation frequency characteristics, the single-propeller UAV emitted the harshest and most pulsating sound, while the tailsitter had lower values.

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.

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 aircraft 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 according 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.

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.

Aircraft noise impacts a growing number of residents around airports. The impact is estimated using noise models such as the European standard Doc 29. These models make use of empirically derived Noise-Power- Distance tables to estimate noise in the areas around the airport. Correction factors are used to account for directionality effects such as engine installation effect and lateral attenuation. Research comparing measured and modelled directionality of the aircraft noise is limited. This research aims to investigate the potential contribution of directionality effects on differences between measured noise levels, obtained around Schiphol Airport using the NOise MOnitoring System (NOMOS), and noise levels predicted by Doc 29. This is done by considering NOMOS measurements at different locations around the airport and use these to retrieve noise levels at the source, i.e., the so-called standardized levels. These noise levels are then used to map the noise levels in lateral and longitudinal directions. By performing the same procedure for modelled sound levels, it is possible to observe differences in directionality patterns. The thus found directivity effects differ significantly from the currently modelled directivity effects. These insights can be used to increase the accuracy of the Doc 29 model without increasing its complexity.

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.

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.

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.

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.

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.

This manuscript summarizes the main recent research efforts at Delft University of Technology in the field of drone and urban air mobility (UAM) vehicle noise. Illustrative examples are showcased, specifically in terms of acoustic measurements (both in-field and in wind-tunnel facilities), noise modelling (both data-driven and physics-based), and human perception of these sounds. In particular, the measurements feature microphone arrays and acoustic imaging to detect, localize, and isolate drone noise emissions. Regarding drone noise modelling, the proposed approaches cover noise generation, propagation, and acoustic footprint calculation. The evaluation of the human perception of drone noise and the perceived annoyance is another crucial aspect. To this end, psychoacoustic listening experiments are conducted in laboratory conditions and the results are analyzed using perception-based sound metrics. Data from aeroacoustic measurements and synthetic sound auralizations are considered. Combining these three main approaches holistically, the perception-driven design and assessment can be performed by targeting the minimization of the perceived noise annoyance, rather than merely reducing sound pressure levels.

The use of acoustics for drone localisation has gained more interest in recent years. Traditionally, acoustic localisation is done using microphone arrays. The data is processed with methods such as a time-difference-of-arrival approach or beamforming. It has however not been investigated yet if alternative noise monitoring systems can be used for this purpose. The aim of this research is therefore to investigate if noise monitoring systems which output noise directivity can be used for drone localisation. Four SV200A noise monitoring systems were used during a real-life experiment. These systems output the directivity in the XY-plane from 0◦ to 360◦ and in the Z-plane from 0◦ to 180◦ in bins with a width of 11.25◦. During the experiment, a DJI Phantom quadcopter flew a variety of manoeuvres. For performance evaluation, two flight patterns are distinguished: horizontal and vertical flight. Initial results showed limited performance. In the XY-plane, the percentage of time where the correct bin was estimated varied between 20% to 49% depending on the manoeuvre and microphone, whereas in the Z-plane this was only 0.3% to 14%. However, when including the angular bins adjacent to the correct bin, the performance increased significantly. In the XY-plane, the percentages increased to 63% to 98% and in the Z-plane to 29% to 49%. A reason for this increase can be that the GPS receiver of the drone has limited accuracy, which leads to a mismatch between the true flown position and the logged position. Furthermore, due to an offset in internal clocks of the drone and microphones, some microphone estimations were slightly ahead or behind the actual drone movement. In conclusion, these results show that the noise monitoring systems can estimate the location of the drone if some margin in the estimation is taken into account.

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.