14:00
Session 3: UAS/UAM noise modeling 1
Chair: Lin Wang
14:00
20 mins
|
Drone Directivity Measurements under Realistic Flight Conditions in an Anechoic Environment for Spectral and Psychoacoustic Characterization
Felix Hochbaum, Gert Herold, Vanessa R. Kempen, André Fiebig
Abstract: In drone noise research, source directivity represents the missing link between near-source sound generation and community-relevant noise exposure. Accurate directivity information is essential for approximating far-field sound propagation and predicting noise impact under realistic operational conditions, and it is important for physically plausible auralisations. However, determining drone directivities is experimentally non-trivial: indoor measurements are typically constrained by limited space, whereas outdoor campaigns introduce substantial uncertainties due to weather-driven variability, interfering ambient sound, and ground-reflection artefacts. This study presents a measurement campaign in which multirotor UAVs of different sizes, with dimensions of up to 2.6 m (measured from rotor tip to rotor tip), were investigated in an anechoic chamber under realistic flight conditions. A spatially distributed array of 64 microphones was deployed throughout the chamber, surrounding the sources, and enabling measurements of free hovering as well as vertical and forward flight manoeuvres. Drone positions during flight were tracked using a LiDAR scanner. For the evaluation, a backpropagation method was employed to obtain time signals for a defined distance around the drones. In addition to sound pressure level directivity characteristics, sound quality metrics were calculated to complement the analyses by describing the psychoacoustic characteristics of the emission pattern. Selected results for two UAVs will be discussed. The measurement data will serve as input for emission signal synthesis and source directivity modelling, enabling physically plausible drone auralisations for noise impact assessment.
|
14:20
20 mins
|
Gaussian Process-Based Time-Domain Modeling for Multi-Rotor Noise Prediction
Jeongwoo Ko, Minhyuk Kim
Abstract: The developed Gaussian process (GP)-based machine learning framework directly reconstructs time-domain signals, capturing both the temporal and spectral characteristics of multi-rotor noise. The framework's comprehensive validation was initially established using synthesized multi-rotor noise data generated from numerical frameworks. Subsequently, its practical applicability was extended to experimentally measured acoustic pressure data acquired within an anechoic chamber under dynamic operating conditions. Quantitative evaluations demonstrate that the posterior mean closely follows the overall experimental waveform structure, with dominant harmonic peaks accurately recovered, while the raw signal remains within the predictive uncertainty bounds. This study outlines the framework’s current capabilities and discusses future implementations for real-world outdoor noise reconstruction.
|
14:40
20 mins
|
A numerical study on the aeroacoustics of a propeller at low Reynolds number subjected to vortical gusts
Mario Alì, Andrea Piccolo, Riccardo Zamponi, Daniele Ragni, Francesco Avallone
Abstract: Drones operating in an urban environment are exposed to turbulent flows generated by atmospheric boundary layers, obstacles, and temperature gradients. This work investigates the aeroacoustics of a low-Reynolds-number propeller in forward flight subjected to sinusoidal gusts. Very Large Eddy Simulations based on the Lattice Boltzmann Method are performed with the commercial software PowerFLOW. Gusts are introduced through unsteady inlet boundary conditions, ensuring the divergence-free condition. Results show that gusts generate additional tonal noise at frequencies given by a combination of the gust frequency and multiples of the rotational frequency, as a result of an amplitude/phase modulation mechanism.
|
|