Nonlinear reduced-order models for transonic aeroelastic and aeroservoelastic problems

Nonlinear reduced-order modeling approaches have been a promising tool for predicting the transonic aerodynamic and aeroelastic behaviors of aircraft structures. However, the accuracy of the approaches in predicting the transonic limit-cycle oscillations and aeroservoelastic behaviors needs to be further improved. For example, because of the complexity induced by the coupling among deflection of control surfaces, oscillating shock waves, structural vibration, and aerodynamic viscosity, it is difficult to adjust the system order and number of neurons of the Wiener-based reduced-order model. Moreover, the increasing of the system order may lead to overfitting. This paper presents a novel reduced-order aerodynamic modeling methodology based on the theory of nonlinear state-space identification. The accuracy of the method is demonstrated for predicting aerodynamic loads, limit-cycle oscillations, and frequency responses of aeroservoelastic systems at transonic flow conditions. Results are compared with full-order simulations solving the Reynolds-averaged Navier-Stokes equations.