This analysis demonstrates efficient noise variability prediction in urban environments using ray tracing and velocity snapshots, suggesting valuable insights for advanced air mobility vehicles.
Turbulence from densely built urban structures alters the acoustic signature of advanced air mobility (AAM) vehicles, complicating prediction of noise impact. Ray tracing, using instantaneous frozen velocity-field snapshots from time-resolved simulations, provides an efficient approach for estimating turbulence-induced acoustic variability. Comparisons of the equivalent sound level (Leq), variability metric (L10−L90), and transient sound-level fluctuations show good agreement with a time-resolved reference solution, where discrepancies are mainly near the source and ground. Correlation analysis confirms that dominant temporal variability trends are reproduced at most observer locations, demonstrating that the method provides a reliable and computationally efficient framework for assessing AAM noise in urban environments.
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Goyal et al. (2026) studied this question.
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