飞行器非定常气动力稀疏识别建模方法OA

To enable efficient,high-fidelity construction of white-box reduced-order aerodynamic models,a frequency-domain unsteady aerodynamic modeling approach is proposed based on Sparse Identification of Nonlinear Dynamics(SINDy).The proposed method uses simulation data of harmonic aircraft motions at representative amplitudes and frequencies,constructs a candidate function library guided by classical algebraic aerodynamic model architectures,and applies sparse regression to select optimal terms and identify parameters-thereby automatically yielding sparse,highly interpretable reduced-order aerodynamic models.Leveraging both classical algebraic model structures and The-odorsen's unsteady aerodynamic theory,we formulate a globally sampled unified model(SINDyA)and a parameter-varying local model(SINDyB).The approach is validated on two canonical problems-transonic pitch oscillations of the NACA64A010 airfoil and of the CHN-T1 aircraft-using lift and pitching-moment coefficients as modeling targets.Results indicate that the identified models require only a small number of dominant terms to capture the key nonlinear and hys-teretic features of the unsteady aerodynamics;the SINDyB model,which performs local interpolation of coefficients,achieves higher prediction accuracy.Because the pitching-moment coefficient exhibits stronger nonlinearity,its predic-tion proves markedly more challenging than that of lift.The models predict aerodynamic responses accurately under small-amplitude excitations,while performance degrades for large-amplitude,high-frequency cases.These findings demonstrate the promise of symbolic machine-learning methods for constructing high-accuracy,interpretable unsteady aerodynamic models and highlight their potential for engineering application.