The growing adoption of electric vehicles, known for their quieter operation compared to internal combustion engine vehicles, raises concerns about their detectability, particularly for vulnerable road users. To address this, regulations mandate the inclusion of exterior sound signals for electric vehicles, specifying minimum sound pressure levels at low speeds. These synthetic exterior sounds are often used in noisy urban environments, creating the challenge of enhancing detectability without introducing excessive noise annoyance. This study investigates the design of synthetic exterior sound signals that balance high noticeability with low annoyance. An audiovisual experiment with 14 participants was conducted using 15 virtual reality scenarios featuring a passing car. The scenarios included various sound signals, such as pure, intermittent, and complex tones at different frequencies. Two baseline cases, a diesel engine and only tyre noise, were also tested. Participants rated sounds for annoyance, noticeability, and informativeness using 11-point ICBEN scales. The findings highlight how psychoacoustic sound quality metrics predict annoyance ratings better than conventional sound metrics, providing insight into optimising sound design for electric vehicles. By improving pedestrian safety while minimising noise pollution, this research supports the development of effective and user-friendly exterior sound standards for electric vehicles.

Distributed propulsion systems are developed to power a new generation of aircraft. However, it is not known yet which noise emissions these propulsion systems produce, which psychoacoustic characteristics such systems exhibit, and how the generated noise is perceived. This paper investigates how fans with fewer stator than rotor blades affect the noise perception of a distributed propulsion system intended for an urban air mobility vehicle, which is equipped with 26 low-speed ducted fans. Three fan designs with different tonal to broadband noise ratio and opposite dominant noise radiation directions are examined. An analytical process is applied to determine the noise emission, propagate the sound through the atmosphere, auralize the flyover signals, and calculate psychoacoustic metrics. A validation and comparison with A320 turbofan engines at takeoff is provided. The results indicate that the distributed propulsion system generates noise signatures with complex directional characteristics and high sharpness. By applying tonal noise reduction mechanisms at source, a significant effective perceived noise level reduction is achieved for the considered fan stages with fewer stator than rotor blades. In addition, tonality, loudness and roughness are reduced well above one noticeable difference compared to a baseline fan and similar or even lower values are achieved than with turbofans.

This study investigates the relationship between sound quality metrics (SQMs) and noise annoyance caused by airborne wind energy systems (AWESs). In a controlled listening experiment, 75 participants rated their annoyance on the International Commission on Biological Effects of Noise (ICBEN) scale in response to recordings from in-field measurements of two fixed-wing and one soft-wing ground-generation AWES. All recordings were normalized to an equivalent A-weighted sound pressure level of 45 dBA. The results revealed that sharpness was the only SQM predicting participants' annoyance. Fixed-wing kites, characterized by sharper and more tonal and narrowband sound profiles, were rated as more annoying than the soft-wing kite, characterized by higher loudness values. In addition, the effect of some SQMs on annoyance depended on participant characteristics, with loudness having a weaker impact on annoyance for participants familiar with AWESs and tonality having a weaker effect on annoyance for older participants. These findings emphasize the importance of considering psychoacoustic factors in the design and operation of AWESs to reduce noise annoyance.

The influence of ambient noise in the perception of wind turbine noise is evaluated in this exploratory study. For this purpose, experimental field measurements of an NTK wind turbine at different wind speeds and background noise levels are considered. Synthetic wind turbine noise auralizations are then computed to replicate the weather and operational conditions during the experiments. Different background noise recordings were then synthetically added to the simulated auralizations to investigate the effect in sound quality metrics, such as loudness, roughness, or the psychoacoustic annoyance model by Zwicker. A least-squares analysis was applied to the resulting sound signals. It was found that adding background noise to the auralisations notably reduced the differences in metrics between simulations and experiments. However, the behaviour with respect to the A-weighted signal-to-noise ratio then becomes background-noise dependent and, hence, more challenging to predict. Therefore, for perceptual studies, it is recommended to use experimental recordings with low background noise as a ground truth.

The human perception of two wind turbines of different sizes, a small NTK turbine and a larger NREL model, was evaluated through their synthetically auralised sound. A wide range of wind speed conditions and observer locations was considered. The simulated sounds were analyzed using equivalent sound pressure levels and psychoacoustic sound quality metrics. Moreover, listening experiments were conducted to evaluate the human response to the same sounds. The least-squares models fitted to the results provided scaling laws for the different sound metrics as a function of wind speed (divided into low- and high-speed regimes) and distance to the observer. At lower wind speeds, the NREL turbine’s noise and annoyance levels increase faster with increasing wind speed than the NTK turbine. The results of the NREL turbine at high wind speeds seem to indicate that turbulent boundary layer trailing-edge noise contributes more to annoyance than leading edge turbulent inflow noise. In the listening experiments, the larger wind turbine was perceived roughly 30% more annoying than the smaller one for the same conditions. The equivalent A-weighted sound pressure level and the psychoacoustic annoyance model by Zwicker were reported to closely represent the annoyance ratings reported in the listening experiment.

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.

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.

Correction Notice In the Acknowledgements section the project VIRLWINT should also be listed. The following statement should be included: This publication is part of the project VIRLWINT (Virtual acoustical twin of distributed propulsion systems) funded by the German Aerospace Center (DLR). The affiliation for author S. Schade should be changed to: German Aerospace Center, Bismarckstraße 101, 10625 Berlin, Germany The affiliation for author P. Ratei should be changed to: German Aerospace Center, Hein-Saß-Weg 22, 21229 Hamburg, Germany The affiliation for author S. Bartels should be changed to: German Aerospace Center, Linder Höhe, 51147 Köln, Germany The affiliation for author R. Jaron und A. Moreau should be changed to: German Aerospace Center, Bismarckstraße 101, 10625 Berlin, Germany.

New aircraft concepts with distributed propulsion systems are currently developed for urban and regional air mobility. Typically, these propulsion systems consist of a large amount of propulsors, such as ducted fans or propellers. As the emitted sound fields of individual propulsors may interact with each other, the noise emission is characterized by interference effects. These interactions change the psychoacoustic characteristics of distributed propulsion systems compared to conventional designs with two or four engines. Therefore, in this paper acoustic interactions due to rotational speed fluctuations are investigated for a distributed propulsion system equipped with 26 ducted, low-speed fans. Synthesized flyover sounds are generated using an analytical auralization process for different ranges of rotational speed fluctuations and the impact on noise immission as well as psychoacoustic sound quality metrics is assessed. Our study indicates that temporal fluctuations decrease with increased rotational speed fluctuations. Time signals are smoothed when speed fluctuations are applied, resulting in lower values for psychoacoustic loudness, tonality and fluctuation strength.

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.

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.

Vertical-axis wind turbines (VAWTs) are considered promising solutions for urban wind energy generation due to their design, low maintenance costs, and reduced noise and visual impact compared to horizontal-axis wind turbines (HAWTs). However, deploying these turbines close to densely populated urban areas often triggers considerable local opposition to wind energy projects. Among the primary concerns raised by communities is the issue of noise emissions. Noise annoyance should be considered in the design and decision-making process to foster the social acceptance of VAWTs in urban environments. At the same time, maximising the operational efficiency of VAWTs in terms of power generation and actuation effort is equally important. This paper balances noise and aero-servo-elastic performance by formulating and solving a multi-objective optimisation problem from a controller calibration perspective. Psychoacoustic annoyance is taken as a novel indicator for the noise objective by providing a more reliable estimate of the human perception of wind turbine noise than conventional sound metrics. The computation of the psychoacoustic annoyance metric is made feasible by integrating it with an accurate and computationally efficient low-fidelity noise prediction model. For optimisation, an advanced partial-load control scheme – often used in industrial turbines – is considered, with the Kω2 controller as a baseline for comparison. Optimal solutions balancing the defined objectives are identified using a multi-criteria decision-making method (MCDM) and are subsequently assessed using a frequency-domain controller analysis framework and mid-fidelity time-domain aero-servo-elastic simulations. The MCDM results indicate the potential application of this controller in small-scale urban VAWTs to attain power gains of up to 39 % on one side and to trade off a reduction in actuation effort of up to 25 % at the cost of only a 2 % power decrease and a 6 % increase in psychoacoustic annoyance on the other side compared to the baseline. These findings confirm the flexible structure of the optimally calibrated wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller, effectively balancing aero-servo-elastic performance with noise emissions and marking the first instance of integrating residential concerns into the decision-making process.

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.

Optimal procedure design through high-precision navigation is one of the most effective ways towards sustainable aviation. By re-designing approach procedures, it is possible to reduce the noise exposure on the ground and minimize the amount of population negatively affected by it in the near-airport areas. In this study, an optimal Required Navigation Performance Authorisation Required (RNP AR) approach procedure designed using statistical historical wind data for minimum noise impact is presented. An analytic comparison of the existing and designed RNP AR procedure around Landvetter Airport in Gothenburg (Sweden) is performed in terms of sound exposure level contours, amount of affected population, auralization, and psychoacoustic sound quality metrics. It is demonstrated that, by re-constructing the lateral profile and allowing the aircraft to turn near the runway, a significant reduction of over 1/3 in the number of people initially affected by noise exceeding 70 dB(A) in terms of sound exposure level can be achieved. However, this comes at the expense of shifting the noise contour towards urban areas that were not previously affected. Auralization and perception-based analyses are used to evaluate the perceived annoyance by the people in the near-airport area, and it is shown that a relatively small reduction in overall psychoacoustic annoyance can be achieved by the new procedure.

This preliminary study evaluates the expected psychoacoustics annoyance of an array of distributed propellers operating at different synchrophasing angles between the propeller blades. The study, conducted in a hybrid test section wind tunnel, focused on a three-propeller array in a tractor configuration. The noise emissions were measured using a phased microphone array. Both conventional noise metrics and state-of-the-art psychoacoustic metrics were calculated, enabling a thorough analysis of the noise characteristics produced by these configurations and their expected noise perception. The study found that the relative blade-phase angle significantly influences the levels of the conventional noise metrics and psychoacoustic metrics. These results highlight the potential of controlling relative blade-phase angles to reduce noise annoyance. They also emphasize the importance of conducting psychoacoustic analysis in design configurations like the one examined here.

The current study reports the results of a psychoacoustic listening experiment investigating the human response to the noise emissions from various types of drone flyovers, recorded during acoustic field experiments. The investigation covers six quadcopters with single propellers, a quadcopter with counterrotating propellers, and two types of hybrid electric vertical take-off and landing (eVTOL) drones. These recorded audio samples were employed in a dedicated listening experiment conducted at the Psychoacoustic Listening Laboratory (PALILA) in the faculty of Aerospace Engineering of Delft University of Technology, involving 57 participants. The two eVTOL drones were perceived as considerably less annoying than their quadcopter counterparts, whereas the coaxial-propeller quadcopter was found to be the most annoying drone. Strong correlations were found between the mass and volume of the quadcopter drones and the annoyance ratings from the listening experiments. Psychoacoustic annoyance metrics from different models proved to predict the perceived noise annoyance more accurately than conventional sound metrics typically employed in noise assessment.

Aeroacoustic testing in wind tunnels is crucial for understanding and mitigating the noise generation mechanisms in several devices while maintaining satisfactory aerodynamic performance during the design stage. However, current aeroacoustic measurements in closed-section wind tunnels face challenges in terms of installation of acoustic sensors, due to the effect of the boundary layer of the wind tunnel walls, and accuracy. To address these issues, the proposed methodology integrates advanced signal processing techniques and cost-effective and limited alterations in a closedsection wind tunnel. Different setup configurations combined with the use of a microphone array consisting of 88 microphones, recessed behind an acoustically transparent stainless steel mesh, have led to significant improvements in signal-to-noise ratio and measurement accuracy compared to the baseline single microphone aeroacoustic testing capabilities. These configurations included a perforated panel with incorporated windscreens, a perforated panel with melamine foam rings, and the addition of melamine foam panels behind the array and inside the wind tunnel, along one of its walls. In general, the proposed approach enables the identification of noise sources with a signal-to-noise ratio of at least −10 dB. Additionally, the utilisation of advanced beamforming techniques (CLEAN-SC and DAMAS) in post-processing yields clearer outcomes. Finally, the effectiveness of the setup was evaluated, using a realistic test model, resulting in an approximate 15 dB improvement in peak prominence, with respect to single-microphone measurements, of a tonal flow-induced noise source due to the higher number of microphones and the application of beamforming.

The attachment of porous media to a blunt trailing edge (TE) can significantly suppress vortex shedding processes and the related tonal noise, yet the near-wall and internal flow fields of porous media are difficult to analyze experimentally and rely on numerical simulations to elucidate the internal flow features. A structured porous trailing edge (SPTE) has been recently designed that follows a methodology of a structured porous coated cylinder. The SPTE acoustic response was compared against randomized porous media with 10 and 30 pores/in. in an anechoic wind tunnel over a range of flow velocities. Acoustic beamforming revealed that the dominant acoustic sources were at the end of the solid plate, even when a porous TE was attached. A region of integration was used to extract acoustic spectra without additional noise sources, revealing that the SPTE possesses superior noise reduction capability. Dipolar directivity patterns were observed at the vortex shedding frequency for each TE, and the coherence between microphones revealed the complex acoustic propagation of the high-frequency content. A wavelet analysis revealed how the SPTE breaks periodic vortex shedding cycles into smaller cycles over a wider frequency range, leading to an overall noise reduction relative to the other TEs.

Current validation of wind turbine noisemodels primarily focuses on sound levels averaged over time, typicallyexpressed in metrics such as the equivalent A-weighted sound pressure level(LA,eq). Whereas valid for regulatory purposes, these methods are notsufficient for psychoacoustic research, as time-averaged levels alone do notfully explain the measured noise annoyance. Therefore, this research aims toestablish whether psychoacoustic sound quality metrics (SQMs) provideadditional value when analysing wind turbine noise models. This work employsthe Horizontal Axis Wind turbine simulation Code 2 (HAWC2) to generate noisesource spectrograms, which are propagated through the atmosphere with aGaussian beam-tracing approach. The final sound signals are retrieved throughan inverse Short-Time Fourier Transform (iSTFT). This methodology is applied toa case study featuring a stall-controlled, horizontal-axis, Nordtank NTK 500/41wind turbine. The results are evaluated against measurements by consideringLA,eq, SQMs, and a comparative listening experiment. The LA,eq metric shows aconsistent underprediction of the simulations with respect to measurements,which is partly explained by a ground reflection modelling error. In thehigh-frequency range, stall noise is known to be significantly underpredictedby the aero-acoustic simulation model. This usually translates in increasingdiscrepancies between measurements and models as the wind speed increases. Thecomparative listening experiment confirms that participants experience thesimulations and the measurements as significantly different. The differenceratings show a good agreement with the differences in the psychoacousticannoyance and loudness metrics. It is more difficult to relate the results fromthe listening experiment to LA,eq. These findings confirm that an evaluationwith psychoacoustic metrics next to conventional methods provides additionalvalue in validating wind turbine noise models for human perception research.