Over the last decade, there has been a marked increase in the use of drones for various applications including emergency and natural disaster response, payload delivery, aerial imaging and surveillance. There are currently many private and public initiatives that aim to further increase the number of drones and diversify their tasks, offering many associated benefits such as reduction in emissions by replacing traditional and more polluting options with electric unmanned aerial vehicles (UAVs). However, several challenges have to be addressed before a broader implementation is accomplished. One of such challenges is the reported noise annoyance produced by UAVs. This study focuses on the development of data-driven models to predict the dynamics of noise metrics during real UAV operations. Extensive outdoor experimental campaigns were conducted, where array-based measurements were recorded during several manoeuvres performed by different types of drones. Beamforming techniques were applied to improve data quality and signal-tonoise ratio (SNR), and to synchronize telemetry and acoustics data streams. Using the improved experimental data, an initial machine learning model was developed to predict the backpropagated OSP L as function of telemetry-derived operational parameters for ascent, hover, and descent. The model managed to accurately predict the experimental data, and it was found that the elevation angle was the most important predictor of OSP L for the considered manoeuvres.

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.

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.

Over the last decade, the aerospace industry has experienced a remarkable growth in the use of Unmanned Aerial Vehicles (UAVs) due to their low manufacturing and operational costs, adaptability, and scalability. In the context of climate change and desired emissions targets, UAVs are realistic options to complement the current freight transportation methods, and in the future, as an viable option for human mobility. UAVs are also starting to gain a place in emergency response, both in urban and rural areas, where aid or surveillance capabilities are required in short notice and a fast response is crucial. However, an important obstacle towards their broader use is the expected noise annoyance caused by UAV operations. In this study, we combine extensive outdoor measurements of drone noise with statistical modelling to predict acoustic metrics relevant to determining acoustic annoyance. Array-based measurements combined with GPS positioning information of an eVTOL fixed-wing UAV are studied under realistic operational conditions in an open field. The linear least squares theory is used to establish an empirical model and identify a set of control parameters, including acceleration and velocity, that serve as effective predictors of noise output. The results pave the way for the establishing a model that predicts drone noise for different UAV types and for different operational conditions.