To evaluate flight performance and aerodynamic characteristics of a dual-spin projectile, the coupled computational fluid dynamics and rigid body dynamics (CFD/RBD) method is commonly used, which can simultaneously solve the flight mechanics and flow field. However, the efficiency is compromised by the large number of CFD calculations required. This paper develops an unsteady aerodynamic modeling method that combines deep neural networks and transfer learning, which can accurately predict unsteady aerodynamics of dual-spin projectiles under varying initial conditions. Considering the influence of flight state and aerodynamic data from short-term historical moments, we integrate them as input features of the aerodynamic model to reduce the impact of long-term historical data. To enhance the model generalization under varying initial conditions, we fine-tune the built aerodynamic model using small amounts of data under new conditions by transfer learning. The proposed method is validated through interpolated and extrapolated prediction cases, respectively. The results indicate that the proposed method can achieve better accuracy and generalizability than long short-term memory neural networks and autoregressive moving average method in unsteady aerodynamic modeling of the dual-spin projectile. By coupling the flight dynamics equations with the aerodynamic model in the time domain, the flight simulation only takes a few seconds, which can reduce computing time by three orders of magnitude compared to the coupled CFD/RBD method.

中文翻译：

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基于DNN和迁移学习的双旋弹丸非定常气动建模与飞行轨迹模拟


为了评估双旋弹的飞行性能和气动特性，通常采用耦合计算流体动力学和刚体动力学 （CFD/RBD） 方法，可以同时求解飞行力学和流场。然而，由于需要大量的 CFD 计算，效率会受到影响。本文开发了一种结合深度神经网络和迁移学习的非定常空气动力学建模方法，可以准确预测不同初始条件下双旋弹的非定常空气动力学。考虑到飞行状态和短期历史时刻的空气动力学数据的影响，我们将它们整合为空气动力学模型的输入特征，以减少长期历史数据的影响。为了增强不同初始条件下的模型泛化，我们通过迁移学习在新条件下使用少量数据对构建的空气动力学模型进行微调。所提出的方法分别通过插值和外推预测案例进行了验证。结果表明，在双自旋弹丸的非定常空气动力学建模中，所提方法可以实现比长短期记忆神经网络和自回归移动平均法更好的精度和泛化性。通过在时域中将飞行动力学方程与空气动力学模型耦合，飞行仿真只需几秒钟，与耦合的 CFD/RBD 方法相比，可以将计算时间缩短三个数量级。