航空学报2026，Vol.47Issue(16)：37-55,19.DOI:10.7527/S1000-6893.2025.32764
飞行器非定常气动力稀疏识别建模方法
Sparse identification modeling method for unsteady aerodynamics of aircraft
摘要
Abstract
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.关键词
非定常气动力/机器学习/稀疏识别/CFD/降阶模型Key words
unsteady aerodynamics/machine learning/sparse identification/CFD/reduced-order model分类
航空航天引用本文复制引用
童金阳,寇家庆,张伟伟..飞行器非定常气动力稀疏识别建模方法[J].航空学报,2026,47(16):37-55,19.基金项目
国家重点研发计划(2024YFB3310401) （2024YFB3310401）
国家自然科学基金(U2441211) （U2441211）
中央高校基本科研业务费专项资金(G2024KY05101) （G2024KY05101）
陕西省自然科学基础研究计划(2025JC-YBQN-087) （2025JC-YBQN-087）
飞行器基础布局全国重点实验室开放基金(JBGS-202504) National Key Research and Development Program of China(2024YFB3310401) （JBGS-202504）
National Natural Science Foundation of China(U2441211) （U2441211）
Fundamental Research Funds for the Central Universities(G2024KY05101) （G2024KY05101）
Natural Science Basic Research Program of Shaanxi Province(2025JC-YBQN-087) （2025JC-YBQN-087）
Open Fund of the National Key Laboratory of Aircraft Configuration Design(JBGS-202504) （JBGS-202504）
