Transonic Aerodynamic–Structural Coupling Characteristics Predicted by Nonlinear Data-Driven Modeling Approach

Abstract

Accurate prediction of nonlinear aerodynamics is essential for the transonic aeroelastic analysis of flight vehicles. Though reduced-order aerodynamic models are cheap and reasonable tools, it is still a tough problem to accurately evaluate the unsteady pressure distributions on the surface of an elastic structure. This paper presents a nonlinear data-driven modeling approach based on the high-fidelity simulations in the following three steps. The first step is to compute the dominant modes of unsteady pressure distributions through the proper orthogonal decomposition. The pressure snapshots used for the feature extraction are sampled under a multilevel sine-sweep excitation. The second step is to obtain the low-dimensional temporal dynamics of the coefficients of these modes via polynomial nonlinear state-space identification. The linear estimation implemented by employing the dynamic mode decomposition with control algorithm serves as the initialization of the nonlinear optimization. The third step is to reconstruct the unsteady pressure distributions under arbitrary structural excitation from the temporal coefficients. The paper validates the approach via two numerical examples of the transonic aerodynamic–structural coupling problem. One is an NACA0012 airfoil, and the other is an AGARD 445.6 wing. The examples show that the proposed approach exhibits both accurate and robust performance in the prediction of unsteady pressure distributions, aerodynamic forces, and aeroelastic responses. In particular, the approach well predicts the physical features at the fluid–structure coupling interface, previously neglected in the system identification of aerodynamic systems. Therefore, the approach serves as a promising tool for data-driven aeroelastic analysis.