Layered reduced-order models for nonlinear aerodynamics and aeroelasticity

Abstract

A layered reduced-order modeling approach for nonlinear unsteady aerodynamics comprising both linear and nonlinear characteristics is developed. The constructed reduced-order model (ROM) with a parallel connection structure is composed of a linear surrogate model and a nonlinear one. The linear autoregressive with exogenous input (ARX) model is constructed by a training case at small amplitude, while the nonlinear radial basis function neural network (RBFNN) model is identified to model the residual nonlinear responses from the second training case under large amplitude motions. Outputs from both models are superimposed to obtain the total aerodynamic coefficients. Either quasi-steady or unsteady layered model can be constructed by introducing only input delay or considering both input and output delayed effects. The present approach is tested by predicting the unsteady aerodynamic forces, limit cycle oscillations and flutter behaviors of a transonic NACA 64A010 airfoil. Results show that both the quasi-steady and the unsteady models agree well with those from high fidelity simulations within a wide range of amplitude and frequency, and the LCO trends are reasonable after the models are coupled with the structural equations of motion. A better agreement is achieved by the unsteady layered model, even though this model may sometimes become unstable.