The increase in flight volumes in the aviation industry has significant socioeconomic implications that affect different aspects of our communities and economies. Although it has great economic benefits, it also causes annoyance and disturbance to communities living near airports. The latter requires understanding and prediction of the varying noise levels generated by various aircraft types. Noise assessment on a fleet level is traditionally achieved by using prediction models such as the DOC29. Such models need to be validated using real measurements. For Amsterdam Schiphol Airport, the so-called NOMOS (Noise Monitoring System) with 39 measurement stations is used for this purpose. We analyze the time series of these stations, collecting annual data for the period from 2006 to 2023. The main objective is to determine how the aircraft-generated noise at these stations can be assigned to 13 different aircraft types, taking into account the different noise levels produced by each aircraft type. This is performed by time series analysis of individual stations and the averaged time series over all stations. The results from two least-squares methods, namely unconstrained least squares (LS) and a proposed bounded least squares subject to weighted constraints (BLS + WC), are compared. The constraints are based on certification data as prior information in the least squares method, which is expected to enhance the model's performance. Based on the above two least squares methods, predictions are performed for 2022 and 2023. The results clearly demonstrate the superiority of the BLS + WC over the LS method. We further extend our analysis to predict noise levels for a hypothetical future year with more newer aircraft models. The results indicate a substantial reduction in the noise level compared to 2023. These findings can thus underscore the effectiveness of the proposed method in outperforming the LS and highlight the model's capability to forecast the impact of fleet modernization on noise reduction.

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.

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.

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.

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.

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.

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.

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 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.

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.

The noise emissions of an optimized low-noise drone propeller design were measured experimentally using a polar array consisting of fifteen microphones within a circular arc covering 105 degrees. The experiments were conducted in the anechoic chamber at the Faculty of Aerospace Engineering at the Technion - Israel Institute of Technology. A comparison between the optimized low-noise drone propeller design and the baseline case (a commercial off-the-shelf APC 14 x5.5 propeller) was performed using conventional sound metrics (e.g. equivalent A-weighted sound pressure level or perceived noise level), as well as state-of-the-art psychoacoustic sound quality metrics (loudness, sharpness, tonality, roughness, and fluctuation strength). These sound quality metrics (SQMs) were also combined into a global psychoacoustic annoyance metric to assess the predicted noise annoyance a human observer would experience. Despite increasing noise levels at the lower frequencies, the optimized configuration presents consistently lower noise emissions in the directivity angles considered for all the sound metrics employed. These results encourage further research into the perceptioninfluenced design of drone propellers by focusing on psychoacoustic metrics that capture human hearing more accurately than conventional sound metrics typically used in certification.

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.

Turbofan engines are still one of the loudest noise sources of modern commercial aircraft. Noise radiation from the engine is highly directional and it is, therefore, important to obtain this directivity pattern from microphone-array data from static engine noise tests. Recently, Sijtsma has recently shown that the deconvolution method CLEAN-SC is able to break down engine noise sources and that it is capable of determining the directivity of these sources in the far field using measurements of a DGEN380 static engine test. This study further investigates this capability of CLEANSC by performing acoustic experiments under controlled conditions in the anechoic chamber at the faculty of applied sciences at Delft University of Technology. The experiments consist of a variety of tests with 2 different speakers placed inside an aluminum cylindrical pipe simulating an engine. In these experiments, the location and directivity of the individual noise sources can be measured separately, so that the CLEAN-SC results can be compared to the ground-truth reference. Moreover, the results are also compared with other acoustic imaging methods, such as DAMAS and conventional beamforming.

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.

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.

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.

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.

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.

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.

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.