Data-based Nonlinear Aeroelastic Modeling

The investigation of flutter and aeroservoelastic stability through flight testing is an essential part of aircraft certification. The stability boundary prediction is especially difficult when the instability is associated with nonlinearity in the dynamics. This paper presents an approach for the characterization of the nonlinear dynamics by non-iterative identification algorithms. Two different block-oriented nonlinear models are considered in this work to augment existing linear models with nonlinear operators derived by analyzing flight data. Central in the identification of block-oriented nonlinear models is the use of the apriori set of orthonormal bases tuned with the dynamics of the aeroelastic/aeroservoelastic plant. In both cases, we propose a method to generate the orthonormal bases that is based on the cascade of input-normal balanced state-space realizations of all-pass filters. Case studies using a simulated structurally nonlinear prototypical two-dimensional wing section and actual F/A-18 AAW Ground Vibration Test (GVT) data are presented.