Global Nonlinear Aerodynamic Reduced-Order Modeling and Parameter Estimation by Radial Basis Functions

This work presents a novel global reduced-order modeling and parameter estimation of a maneuvering aircraft up to poststall angles of attack using radial basis functions. A computational fluid dynamics approach is adopted to accurately predict the flow field around the maneuvering standard dynamic model. High-amplitude chirp motions are used to excite the aerodynamic system in both longitudinal and lateral-directional axes up to poststall conditions. Subsequently, a radial basis function neural network is employed to construct a nonlinear aerodynamic model from 20% of the numerical simulation data. Next, a continuous wavelet transform is applied to gain insight into the frequency-time behavior of the aerodynamic moments. Based on the results, the network can predict the great unsteady aerodynamic characteristics of the aircraft under deep dynamic stall and coupled yaw-pitch motion over the unseen test data, compared with the entire numerical simulations. Moreover, instantaneous stability derivatives are computed, which are required for design of a maneuvering aircraft flight control system. A great dependency of the stability derivatives on reduced frequency and angle of attack is perceived in the results. In addition, high coupling is seen in lateral-directional derivatives, expressing the strong influence of the angle of attack on the associated moment coefficients.

Automated summary

This study presents a novel global reduced-order modeling and parameter estimation of a maneuvering aircraft using radial basis functions. A computational fluid dynamics approach accurately predicts the flow field, a neural network constructs a nonlinear aerodynamic model, and stability derivatives are analyzed for their dependency on reduced frequency and angle of attack, as well as the influence of angle of attack on moment coefficients.