A wavelet-based separation method for tonal and broadband components of low Reynolds-number propeller noise

S. Meloni*, E. de Paola, E. Grande, D. Ragni, L. G. Stoica, A. Di Marco, R. Camussi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Propeller noise generally exhibits a rich mixture of tonal and broadband components related to different physical mechanisms. Specifically, the tones are characterized by having deterministic and persistent characteristics, while the broadband counterpart has random behaviour. The separation is essential for the experimenters as they provide information on the different noise sources. In this framework, the study presents a novel wavelet-based method able to separate the noise emitted by a low Reynolds number propeller into its tonal and broadband components. The technique is applied to an isolated rotor operating under different loading configurations, including hover and cruise conditions. The acoustic pressure data are obtained in the anechoic tunnel (A-tunnel) of the TU Delft low-speed laboratory with a near-field polar and azimuthal distribution of microphones. The method is based upon a threshold varying procedure that separates the tonal and broadband components through the computation of two-point statistics. Advantages and drawbacks with respect to other methodologies already known from the literature are discussed. The application of the method provides the spectral content of the tonal and broadband components as well as the different polar and azimuthal directivity. Specifically, the observed dipole-like shape directivity for the tonal part and flatter broadband OASPL, confirm that the method can provide quite a good separation. Furthermore, the overall flow behaviour is inferred from the decomposition and validated through benchmarked flow visualizations.

Original languageEnglish
Article number044007
Number of pages13
JournalMeasurement Science and Technology
Issue number4
Publication statusPublished - 2023


  • aeroacoustics
  • drone
  • PIV
  • propeller noise
  • wavelet decomposition


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