Aeroelastic System Identification for Flutter Prediction via Multi-Output Autoregressive Modeling

The paper presents a study of aeroelastic system identification and flutter prediction in a wind tunnel test. Multioutput autoregressive moving-average and multi-output autoregressive models are fitted to acceleration and strain data and their correlation functions, respectively, to generate models that account for the dependencies between the different measured data. The modal frequencies and damping values are extracted from these models and used to assess the flutter margin at different airspeeds and predict the flutter onset speed. The method is applied in a flutter prediction wind tunnel study of a full-scale, half-span aeroelastic demonstrator aircraft using multiple acceleration data and strain data from fiber-optic strain sensors. Both models provide a smooth variation of the modal parameters (frequencies and damping values), from which it is easy to identify the critical aeroelastic modes that coalesce in flutter. Based on the different data sets, all models accurately predict flutter onset at approximately the same airspeed. An advantage of the proposed system identification approaches is that they concurrently account for the data from multiple sensors, avoiding the need to average the modal dynamics estimated from different sensors. The proposed method can be used to support safe flutter tests, as accurate system identification leads to accurate flutter prediction based on structural data recorded at safe airspeeds, remote from flutter.