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- W3186424965 abstract "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." @default.
- W3186424965 created "2021-08-02" @default.
- W3186424965 creator A5014593887 @default.
- W3186424965 date "2021-11-01" @default.
- W3186424965 modified "2023-10-14" @default.
- W3186424965 title "Global Nonlinear Aerodynamic Reduced-Order Modeling and Parameter Estimation by Radial Basis Functions" @default.
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- W3186424965 doi "https://doi.org/10.1061/(asce)as.1943-5525.0001313" @default.
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