结合系统辨识和迁移学习的高速旋转弹气动力建模方法

Computational fluid dynamics andrigid body dynamics(CFD/ RBD) coupling simulation is a common method for evaluating the flight performance of spinning projectiles, which is quite inefficient due to huge amount of CFD simulations. An efficient and accurate aerodynamic model with strong generalization ability is established to significantly improve the simulation efficiency by replacing the CFD module in coupling simulation. An aerodynamic modeling method combining system identification and transfer learning is proposed to address the above-mentioned problem. First, the samples of spinning projectile are obtained by CFD/ RBD coupling simulations under the given initial conditions. Then the autoregressive moving average method is used to build an original aerodynamic model, and the long short-term memory network is utilized to build a state prediction model. In condition of small variation of initial state, the original aerodynamic model remains valid; however in case of large variation of initial state, the state prediction model is transferred to the corresponding initial state, and then an aerodynamic model is built by using the autoregressive moving average method based on the predicted state parameters. The results show that the proposed method is suitable for accurate aerodynamic modeling of high-spinning projectile under the large variations of initial angular velocity and pitch angle. In comparison with the autoregressive moving average method based on direct CFD / RBD coupling simulations, the modeling efficiency of the proposed method is doubled.