Bayesian inference for modal identification in ducts with a shear flow

Abstract

An induct modal decomposition technique is described. The basis for the technique is to consider the decomposition as an inference problem. Using transfer function measurements at the duct walls, a Bayesian inference is conducted to evaluate the acoustic modal coecients in the presence of uncertainties. These uncertainties encompass model errors, microphone measurements error, and uncertainty on the ow prole. The formalism of the direct problem of modal decomposition in a ducted shear ow is rst developed. The case of a circular cross-section duct is then treated without and with a ow, using synthetic noisy signals for the inference problem.

Shear flow effects in ducts: application to direct impedance eduction of acoustic liners

“…To that end, most direct methods rely on the assumption of a uniform mean flow by using Ingard-Myers boundary condition [1], whereas others assume a sheared mean flow and use Pridmore Brown equation [2]. Recently, some studies have suggested and shown that the assumption of uniform flow may introduce errors into the impedance results, either in case of large flow speed [3], 3D computations [4] or large ducts [5]. Nevertheless, most of previous research focused on small cross-section ducts, while extended studies on the effect of shear flow in large ducts are lacking.…”