14:00
Experimental Aeroacoustic Measurements – Field 1
14:00
20 mins
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Commercial Multicopter Propellers in Flight: Noise Directivity, Sound Power, and Efficiency
Julian Benz, Gert Herold, Andreas Gründer, Ennes Sarradj, Stefan Becker
Abstract: For a robust assessment of propeller designs for multicopters, comparable and well-documented acoustic measurements under realistic flight conditions are required. In summer 2025, an extensive measurement campaign was carried out using a ground-mounted microphone array. The acquired acoustic data were combined with onboard-recorded motor performance measurements. A purpose-built quadcopter platform was operated sequentially with various commercially available propellers. The investigated propeller set covers different materials and manufacturing routes, including wooden propellers, injection-moulded plastic propellers (different polymers), and laminated carbon propellers, both solid and as sandwich designs with a foam core. In addition, a dedicated benchmark propeller with fully known and documented geometry was included in the study.
In this contribution, the benchmark propeller is compared to the commercial propellers in hover as well as in straight-and-level forward flight at 10 m/s and 20 m/s. From the microphone array measurements, frequency-dependent radiation directivity patterns are derived and their changes between hover and forward flight are analysed. Sound power quantities are estimated by back-propagating the measured sound field through a propagation model to obtain a source-near description. The spectral analysis separates tonal components (in particular the blade-passing frequency and its harmonics) from broadband contributions.
Alongside the acoustics, the onboard-recorded motor performance data are evaluated during the flight manoeuvres, including rotational speed, voltage, current, and electrical input power, to quantify the power demand for each operating condition. This allows the acoustic radiation to be directly related to the required propulsion power and enables an efficiency-oriented perspective to be included in the assessment. The results provide a consistent, measurement-based comparison across propellers and flight states and show how material and geometry choices influence tonal dominance, broadband levels, modulation, and directivity, while also considering power demand as a practical constraint.
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14:20
20 mins
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Development of an onboard microphone cage for in-flight characterization of drone noise sources
Peter Hartford, Riccardo Zamponi, Christophe Schram
Abstract: Unmanned aircraft systems are becoming increasingly prevalent in urban environments, yet public acceptance remains limited. The mitigation of drone noise emissions, mainly characterized by annoying tonal components from the propellers and broadband noise from the motors and atmospheric turbulence , will play a crucial role in enabling wider adoption of emerging applications. Current approaches for characterizing drone noise emissions generally fall into two categories: field fly-over measurements using ground-based microphone arrays , and laboratory measurements in which individual propellers or entire drones are tested in acoustically treated environments. However, both approaches present several limitations. Field fly-over measurements capture realistic operating conditions and flight maneuvers but are affected by propagation and directivity effects, as well as uncertainties associated with the drone’s position and orientation. Laboratory-based measurements offer improved acoustic control and spatial resolution but typically lack the physical space required to perform representative flight maneuvers.
To address these limitations, an attached microphone cage has been developed with the aim of directly capturing and localizing noise generation sources onboard the drone. The current design, based on the Holybro X500 frame, consists of up to 32 TDK InvenSense ICS-40638 MEMS Microphones. A Pixhawk 6X flight controller is synchronized with an onboard Raspberry Pi 5 to enable simultaneous acquisition of the drone state, including propeller rotational speed, and microphone data. Post-processing techniques will be shown, with potential application of acoustic beamforming or holographic methods4 for source localization and directivity analysis. Finally, the challenges associated with the implementation of an onboard microphone cage are discussed, including requirements for flight stability and the introduction of structural elements that may increase acoustic scattering.
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14:40
20 mins
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Towards accurate UAV source models for noise mapping
Ulf Orrenius, Ulf Tengzelius, Mats Åbom
Abstract: Reliable noise mapping is crucial to managing noise exposure from novel drone traffic, Urban Air Mobility (UAM). Dedicated noise mapping tools, such as SAFTu, NoiseModelling, and SoundPLANnoise, require accurate source models that represent the sound emissions of the drone in operation. In relation to established civil aviation noise modelling standards, such as ECAC Doc. 29 and associated NPD data derived from ICAO Annex 14/16 certification, neither UAV noise certificates nor certification standards are in place to date.
Until certification frameworks are in place, a methodology for deriving source data needs to be developed from voluntary test data. When developing such methodology, the different operation modes associated with VTOL propulsion systems need to be accounted for. Operation at lower altitudes necessitates consideration of entire flight trajectories, including en-route phases, rather than only take-off and landing.
Recent EASA guidelines for in-situ noise measurements of UAVs up to 600 kg represent an important step toward realistic drone noise assessment; however, the omission of take-off and landing measurements, as well as the lack of frequency-dependent data, constitutes a limitation for noise modelling and sound propagation studies. In the present work, it is shown how field-measured noise data can be used to derive acoustic source models with controlled uncertainty. Moreover, simplified physics-based models, based on e.g. vehicle mass and rotor properties such as tip speed, diameter, and pitch are discussed in the context of extrapolating measured acoustic data at a given flight mode. Such models can also be used to support vehicle design optimization work regarding noise generation.
Methodologies for converting limited experimental data sets into source models are also discussed, e.g., when directivity data is limited or missing, or when field test data are affected by ground reflection. Calculation examples are presented, highlighting how field test data, in combination with physical sound generation models, can be used to determine source models to be used in noise maps.
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