Numerical virtual flight investigation for longitudinal maneuver of a generic fighter based on machine learning

作者：Lang Yan, Xinghua Chang, Nianhua Wang, Laiping Zhang, Wei Liu, Xiaogang Deng · 发表于：Physics of Fluids · 年份：2024 · DOI：10.1063/5.0208437 · 被引用次数：8 · 研究领域：Computational Fluid Dynamics and Aerodynamics、Fluid Dynamics and Turbulent Flows、Model Reduction and Neural Networks

Coupled with computational fluid dynamics (CFD), rigid body dynamics (RBD), and flight control system, the numerical virtual flight (NVF) technology can simulate the maneuvering flight process of an air vehicle under control. In this paper, the NVF investigation of longitudinal maneuvers with elevator and thrust vector control is performed for a generic fighter configuration. The rigid dynamic hybrid grid method is taken to realize the motion of the fighter, and the overlapping moving grid technology meets the deflection of the elevator. The Reynolds-averaged Navier–Stokes equations in arbitrary Lagrangian–Eulerian form are coupled with the RBD equations to solve aerodynamics and kinematics problems, while flight control is achieved through an advanced machine learning method. First, the fighter is forced to pitch with the periodic deflection of the elevator, and the unsteady computation is carried out to obtain aerodynamic data. Then, an artificial neural network (ANN) is adopted for aerodynamic identification and modeling, which involves establishing a model between the aerodynamic coefficient and pitching motion parameters. Afterward, the ANN-based NVF is implemented on the basis of the established model and deep reinforcement learning (DRL) is used to design the pitching control law of the fighter. The NVF results based on ANN show that the fighter has a good control effect under the action of the elevator, elevator with open-loop thrust vector, and elevator with closed-l...