16:40
UAS/UAM noise modelling 3
16:40
20 mins
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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 auralizations. 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 artifacts. This study presents a measurement campaign in which multirotor drones 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, the spectral radiation behaviour was examined in greater detail. Sound quality metrics were calculated to complement the analyses by describing the psychoacoustic emission pattern. Selected results for various drones will be discussed. The measurement data will serve as input for emission signal synthesis and source directivity modelling, enabling physically plausible drone noise auralizations for noise impact assessment.
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17:00
20 mins
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Novel noise prediction method of the Urban Air Mobility propeller under perturbations
Nikita Dhiman, Lourenço Tercio Lima Pereira, Frits de Prenter, Damiano Casalino
Abstract: Advanced Air Mobility (AAM) seeks to enable safe, sustainable, and accessible aviation for regional passenger and cargo transportation [1]. Passenger-focused urban operations are commonly referred to as Urban Air Mobility (UAM). The operation of UAM vehicles in proximity to populated areas introduces significant challenges, particularly noise pollution and community annoyance [2]. Existing UAM noise prediction approaches rely mostly on high-fidelity simulations [3] [4], which are computationally expensive, or analysis done in a limited set of flow conditions [5] - [8] and vehicle configuration, e.g., hover, take-off, cruise, and landing [9]. The current noise prediction techniques thus limit the ability to assess more realistic scenarios, such as flow perturbations inherent to real-time environments. In urban operations, the effect of turbulence ingestion, rapid maneuvers can significantly alter the flow conditions over these vehicles and, consequently, their noise emissions
This motivates the development of a noise-prediction methodology capable of providing a quick estimate of UAM vehicle noise under dynamic operation and perturbed flow conditions. This research work aims to address this need. System identification techniques offer a promising framework for developing reduced-order models from limited system data. These methods aim to characterize system dynamics using observed input–output relationships, enabling efficient prediction under varying operating conditions. Of particular relevance to the present study is the Duhamel-based approach, which leverages the system response to a step input to predict its response to arbitrary time-varying inputs through convolution with the derivative of the step response [10]. When combined with high-fidelity simulations for system identification, this approach allows the propeller's dynamics to be represented for any change in rotational speed. This strategy provides a computationally efficient pathway to capture unsteady propeller behavior while retaining the fidelity required for accurate noise prediction in perturbed flow environments. After obtaining the propeller loading under various perturbation conditions using this novel approach, the propeller noise can be evaluated using Farassat Formulation 1A [11] to obtain the changes in noise levels due to the variation of rotational speed. This work explores this methodology and applies it to a reference UAM rotor, comparing it with high-fidelity numerical simulations at varying rpm.
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