Experimental study of urban air mobility noise based on scaled-model tests

This study investigates how ground surface treatment and building presence influence the acoustic field of a hovering unmanned aerial vehicle (UAV) using a controlled scaled-model experimental setup. A tracked dynamically hovering UAV was used as the acoustic source and operated at prescribed source locations, and sound pressure level spectra were measured along a linear microphone array and at local on-structure receiver positions. The corresponding motion-capture records were used to verify the static-hover condition during the acoustic-recording windows. The experiment was designed to provide a controlled model-scale benchmark for relative propagation comparisons rather than to serve as a direct full-scale SPL prediction. Spectral differences were evaluated among four environmental conditions combining rigid or artificial grass-covered ground with building-present or no-building configurations. The artificial grass layer, used as a controlled partially absorptive surface treatment relative to the bare acrylic floor, produced a consistent high-frequency spectral reduction with weak spatial variation along the main array. In contrast, the model building produced a spatially non-uniform effect, with stronger high-frequency reduction in the building-shielded region and much weaker changes in the region with direct source exposure. Both effects were less pronounced at lower frequencies, where diffraction and phase-sensitive interference became more important. The ground–building interaction had limited impact on band-mean levels, although localized frequency-selective deviations remained observable in representative narrowband spectra. The local on-structure observations further showed that near-structure spectra can be sensitive to local source–receiver geometry and UAV hovering height. Overall, the results provide a repeatable scaled-model benchmark for identifying frequency-dependent, spatially structured, and localized spectral features in UAV noise propagation, and support the development and validation of urban air mobility noise prediction methods.