Semi-empirical calibration of remote microphone probes using Bayesian inference
O.K.M. Moriaux (TU Delft - Aerospace Engineering)
Riccardo Zamponi – Mentor (TU Delft - Wind Energy)
Christophe Schram – Graduation committee member (von Karman Institute for Fluid Dynamics)
Daniele Ragni – Graduation committee member (TU Delft - Wind Energy)
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Abstract
Unsteady surface pressures shed light on the turbulent structures of boundary layer flow, which dictate for a large part the aerodynamic and aeroacoustic performance of aerodynamic bodies submersed in a flow. Remote microphone probes (RMP), e.g., pinhole probes, provide advantages compared to flush-mounted probes because of their reduced sensing area.
However, they feature a distinct transfer function (TF) that needs to be taken into account for accurate pressure measurements. Many empirical calibration techniques for such probes introduce spurious resonance into the calibration, which propagate to the measurements. In this study, a semi-empirical calibration method is proposed with the aim of removing the spurious resonance in a physics-driven manner that is less reliant on the operator. Bayesian inversion is used to fit an analytic model for the TF of the RMP to the empirical calibration data.