Efficient prediction of urban air mobility noise in a vertiport environment

Abstract

Efficient calculation of urban air mobility noise footprint in a vertiport environment, considering acoustic effects of various designs, operational conditions, and environmental factors, is essential to limit the noise impact on the community at an early stage. To this purpose, the computationally efficient low-fidelity approach presented by the authors in Fuerkaiti et al. (2022) [11] is extended to calculate the noise footprint of an aircraft in a generic 3D environment. The straight-ray propagator is replaced with a Gaussian beam tracer that accounts for complex source directivity, 3D varying terrain topology, and wind profiles. The reliability of the Gaussian beam tracer has been verified in previous studies by the authors. In this work, it is further extended to include complex source directivity in the presence of a moving medium. Noise sources, obtained using a low-fidelity toolchain, are stored on a sphere surrounding the aircraft and are propagated through an inhomogeneous anisotropic atmosphere. Noise footprints, predicted for different terrain topologies, source directivities, and wind flow conditions, are compared. It is shown that, compared to flat terrain, for the case under investigation, the building blocks increase on-ground noise levels by 5 dB in the illuminated zone due to multiple reflections; they also shield the incoming sound field by creating shadow zones behind the building. The shielding increases with increasing frequency in a quiescent atmosphere. The change between the source directivities, corresponding to the first and second harmonics of the blade passing frequency, results in a difference of up to 40 dB in the noise footprint. The presence of the wind flow can contribute a significant variation in the acoustic footprint by changing the lobes of the footprint pattern and intensifying the noise levels; the variation increases with increasing frequency. Compared to the straight-ray propagator, the present approach reduces the prediction error by 5 dB in the illuminated zones and 35 dB in the terrain shadow zones.