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.

The present study reports a novel wavelet-based method aimed at separating the noise emitted by a single propeller into two contributions, tonal and broadband. An assessment using two different experimental investigations of propellers operating in diverse configurations is presented. The first experiment focuses upon near-field polar microphone array data of a benchmarked low-Reynolds number propeller, in hover and cruise conditions. Measurements were performed in the anechoic tunnel (A-Tunnel) at the low-speed laboratory of TU Delft. The second set of data consists of a test campaign carried out at the Pininfarina Aerodynamic and Aeroacoustic Research Center in Turin (Italy) under the EU funded project ERaCLE. The model comprises a five-bladed propeller installed close to a wing. Pressure signals were acquired using a top-mounted linear microphone array that spans different polar locations. The wavelet-based algorithm able to separate the tonal and broadband contributions through the computation of two-point statistics. The assessment of the decomposition procedure on two very different databases is presented to validate the technique with the aim to extend its range of applications.