Experimental design and stochastic modeling of hydrodynamic wave propagation within cavities for wind tunnel acoustic measurements

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This study investigates how embedding microphones in different cavity geometries along the wall of a wind tunnel reduces the measured turbulent boundary layer pressure fluctuations. The effect of these cavities on the measured signal-to-noise ratio of an acoustic source with flow present was also quantified. Twelve cavity geometries defined by their depths, diameters, chamfer, opening percentage, and mesh covering were tested. The cavity geometries were selected using a design of experiments methodology. The application of design of experiments enabled a statistically sound and efficient test campaign. This was done by applying a D-optimal selection criterion to all potential cavity geometries in order to select 12 cavities to allow for the individual effect of the geometric parameters such as depth and diameter to be quantified with statistical confidence. The resulting wind tunnel test data were fit to a generalized additive model. This approach quantified the relative effect of these parameters on the turbulent boundary layer pressure spectral energy and signal-to-noise ratio while accounting for non-linear frequency dependence. This experimental investigation quantified how much increasing depth reduces the turbulent boundary layer spectral energy and increases signal-to-noise ratio. It also showed that a mesh covering reduces the boundary layer noise by 8 dB. It was also quantified how much reducing the cavity area from the opening of the cavity to the base of the microphone reduces the measured boundary layer spectral energy. Additionally, the model quantified the interactions between the mesh and cavity area as well as the change in area.

Original languageEnglish
Pages (from-to)752-779
Number of pages28
JournalInternational Journal of Aeroacoustics
Issue number8
Publication statusPublished - 1 Nov 2019


  • Beamforming
  • cavity acoustics
  • design of experiments
  • generalized additive model


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