Bayesian identification of acoustic impedance in treated ducts

Abstract

The noise reduction of a liner placed in the nacelle of a turbofan engine is still difficult to predict due to the lack of knowledge of its acoustic impedance that depends on grazing flow profile, mode order, and sound pressure level. An eduction method, based on a Bayesian approach, is presented here to adjust an impedance model of the liner from sound pressures measured in a rectangular treated duct under multimodal propagation and flow. The cost function is regularized with prior information provided by Guess's [J. Sound Vib. 40, 119–137 (1975)] impedance of a perforated plate. The multi-parameter optimization is achieved with an Evolutionary-Markov-Chain-Monte-Carlo algorithm.