A hybrid reduced-order framework for complex aeroelastic simulations

This paper develops a hybrid and parallel-structured reduced-order framework for modeling unsteady aerodynamics, which incorporates both linear and nonlinear system identification methods. To reflect unsteady flow physics, the hybrid model introduces time-delayed output feedback to both linear and nonlinear subsystems. The linear output and nonlinear residual are identified by the autoregressive with exogenous input model and the multi-kernel neural network, respectively. The proposed approach is illustrated here with the reduction of computational-fluid-dynamics-based aeroelastic analysis of a NACA0012 airfoil oscillating in transonic and viscous flows. In particular, we exploit the potential of this model in analyzing complex aeroelastic phenomena including limit-cycle oscillations, the beat phenomenon at high reduced velocities, and nodal-shaped oscillations induced by the interaction between buffet and flutter. Results demonstrate that the proposed approach approximates the dynamically linear and nonlinear aerodynamic characteristics obtained from high-fidelity time-marching methods with a high level of accuracy. This framework can be used as a general reduced-order modeling strategy to represent dynamic systems exhibiting both linear and nonlinear characteristics.