Incremental superposition neural network model for fighter jet spin simulation

Abstract:

To improve the aerodynamic prediction capability during fighter jet spin maneuvers and enhance the simulation accuracy of stable spin motion, a novel neural network model is proposed, leveraging the powerful function approximation capabilities of deep neural networks. This model enables accurate modeling of the unsteady aerodynamic forces during spin maneuvers and achieves high-precision spin attitude prediction through spin-coupled simulation. Focusing on the aerodynamic characteristics of fighter jets in post-stall spin, this study first utilizes the neural network model to achieve high-precision modeling of the unsteady aerodynamic moments observed in vertical wind tunnel tests. Secondly, based on the features of the neural network model and traditional aerodynamic database construction methods, an incremental superposition neural network model is proposed. This model incorporates control surface deflection increments from aerodynamic databases into the neural network, enabling high-precision modeling of unsteady aerodynamic moments under varying control surface configurations. Finally, the resulting model is then coupled with the spin motion equations to conduct stable spin simulations and spin characteristic predictions. The research results indicate that the proposed model effectively captures variations in spin aerodynamics under different control surface combinations. Compared to traditional aerodynamic databases, the aerodynamic moment prediction error is reduced by 77%. Using this model enables high-precision prediction of stable spin characteristics, with the relative error in stable spin period prediction reduced by 34%, demonstrating the engineering effectiveness of machine learning methods in simulating complex aircraft dynamics.