Assessment of Impedance Eduction Methods Using Numerical Simulations and Modal Decomposition Analysis

Acoustic liners are passive devices used to attenuate acoustic waves in turbofan engines. In this sense, impedance eduction techniques are widely used to characterize their acoustic properties. However, measurement uncertainty associated with these techniques is not well assessed yet, leading to variations in educed impedance values depending on the eduction method employed. This study compares two eduction methods- the Mode-Matching (MM) approach and the Kumaresan-Tufts (KT) algorithm-using a numerical database generated through scale-resolved lattice-Boltzmann simulations of an acoustic liner grazed by acoustic waves and a turbulent flow. Results show that both methods yield comparable impedance values, with the MM approach being characterized by higher accuracy at lower frequencies in the presence of grazing flow. The accuracy of the MM method is evaluated by comparing the reconstructed acoustic fields against reference measurements, revealing a peak in the relative error near the liner edges. Additionally, Spectral Proper Orthogonal Decomposition (SPOD) is applied to decompose the acoustic and flow fields into modes, enabling a detailed assessment of higher-order mode impact to the impedance eduction process.