Wind-tunnel study of the autoregressive moving-average flutter prediction method

This paper presents an experimental study of flutter prediction via autoregressive moving-average (ARMA) system identification and the use of a linear stability parameter. System identification at subcritical (preflutter) conditions is based on measured structural responses to excitation by natural air turbulence. The current study investigates the application aspects of the methodology in a dedicated wind-tunnel experiment. An elastic wing was designed and manufactured using rapid prototyping and tested in a subsonic wind tunnel all the way to flutter. Structural responses were recorded by accelerometers, strain gauges, and fiber-optic sensors (measuring strains). The data were filtered, averaged, and used for system identification and flutter prediction. The study focuses on the prediction characteristics and accuracy, method applicability with various dynamic data, and signal processing techniques. It was shown that both accelerations and strains provide good data for system identification and accurate flutter prediction, even when sparse and short records are used. Averaging significantly improves the flutter onset prediction and its confidence bounds. The study results support the feasibility of using the ARMA method for flutter flight test.