Acoustic impedance estimation in grazing flow via mode matching and Bayesian adaptive direct search optimization

The fan noise is a significant source of aircraft engine noise, and the installation of acoustic liners in engine nacelles has been demonstrated to effectively mitigate this noise. The acoustic property of the acoustic liner is characterized by its acoustic impedance, and accurately and stably estimating the acoustic impedance value is crucial for evaluating the liner performance. In this article, a novel acoustic impedance estimation method based on mode matching and Bayesian adaptive direct search optimization is developed to accurately determine the acoustic impedance under grazing flow and high sound pressure level conditions. Firstly, the sound propagation model of the grazing flow duct with an acoustic liner is established by the mode matching method (MMM). Then, an objective error function based on the duct acoustic field is constructed. Finally, the Bayesian adaptive direct search optimization (BADSO) algorithm is applied to estimate the impedance of the acoustic liner. An uncertainty analysis using the Monte Carlo method is conducted through numerical experiments to evaluate the performance of the method under random perturbations. The effectiveness of the proposed method is further validated using NASA benchmark datasets. The results indicate that the proposed method can achieve stable impedance estimation in complex noise environments while maintaining computational efficiency.