Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

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. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.