Reduced-order models for computational-fluid-dynamics-based nonlinear aeroelastic problems

In this work, a nonlinear state-space-based identification method is proposed to describe compactly unsteady aerodynamic responses. Such a reduced-order model is trained on a series of signals that implicitly represent the relationship between the structural motion and the aerodynamic loads. The determination of the model parameters is obtained through a two-level training procedure where, in the first stage, the matrices associated to the linear part of the model are computed by a robust subspace projection technique, whereas the remaining nonlinear terms are determined by an output error-minimization procedure in the second stage. The present approach is tested on two different problems, proving the convergence toward the reference results obtained by a computational fluid dynamics solver in linear and nonlinear, aerodynamic, and aeroelastic applications, whereas the aerodynamic reduced-order models are coupled with the related structural mechanical systems, demonstrating the ability of capturing the main nonlinear features of the response. The robustness of the reduced-order model is then tested considering a series of inputs with varying amplitudes and frequencies outside the range of interest and computing aeroelastic responses with nonnull pretwist angles.