南京航空航天大学学报（英文版）2024，Vol.41Issue(4)：432-443,12.DOI:10.16356/j.1005⁃1120.2024.04.002
基于门控神经网络的大迎角非定常气动力建模
Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack
摘要
Abstract
Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.关键词
大迎角/非定常气动力建模/门控神经网络/泛化能力Key words
large angle of attack/unsteady aerodynamic modeling/gated neural networks/generalization ability引用本文复制引用
邓永涛,程诗信,米百刚..基于门控神经网络的大迎角非定常气动力建模[J].南京航空航天大学学报（英文版）,2024,41(4):432-443,12.基金项目
This work was supported in part by the National Natural Science Foundation of China(No.12202363). （No.12202363）
