Nonlinear Statistical Approach for Aeroelastic Response Prediction

Abstract

Aging aircraft and combat aircraft that carry heavy external stores potentially face problems arising from nonlinearities in structure. An expert data mining system is proposed that is capable of predicting the asymptotic behavior of an aeroelastic system with structural nonlinearities represented by polynomial restoring forces or freeplay models. The input is represented only by a limited set of transient data. The output provides a long-term nonlinear aeroelastic response, and the prediction is made when certain rule-based reasoning conditions are satisfied. An attractive feature of this new approach is that no information about the system parameters is needed. In the prediction module, we propose two methods, based on nonlinear time series models and the unscented Kalman filter. To our knowledge, these approaches have not been reported so far for predicting the long-term nonlinear aeroelastic responses. Compared with the classical extended Kalman filter, the unscented filter does not require differentiability and can be applied to nonlinear aeroelastic models with freeplay and hysteresis. The performances of the expert data mining system are demonstrated for simulated data and wind-tunnel experimental aeroelastic data resulting from a two-degree-of-freedom airfoil oscillating in pitch and plunge.