Parametric Uncertainty Analysis for Impedance Eduction Based on Prony’s Method
Lucas A. Bonomo, André Spillere, Júlio A. Cordioli
Abstract
Impedance eduction techniques are commonly employed to obtain the acoustic impedance of liner test samples in the presence of flow; but, as other experimental techniques, it is subject to measuring uncertainties. This paper investigates the main sources of uncertainty in impedance eduction techniques based on Prony-like algorithms. The Monte Carlo method is used to conduct a parametric uncertainty analysis for each input variable in an analytical model of a test rig. Results suggest that critical variables are the microphone sensitivity and positioning. Different microphone arrays are considered to reduce uncertainty levels. Also, it is observed that, in general, the Kumaresan–Tufts algorithm achieves better accuracy than the original Prony’s method. Finally, the Monte Carlo method is used to evaluate uncertainty levels on NASA benchmark data, and the results corroborate the numerical experiments analysis.