In the present work, a reduced-order modeling (ROM) framework based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. This type of network has a high potential for modeling sequential data, which is favorable for capturing the time-delayed effects associated with unsteady aerodynamics. Therefore, the nonlinear identification procedure as well as the generalization of the resulting ROM are presented. Further, a Monte-Carlo-based training procedure is performed in order to estimate statistical errors. The training data set for the ROM is provided by means of forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulation. Subsequent to the training process, the ROM is applied for the computation of time-varying integral quantities such as aerodynamic force and moment coefficients. The most challenging aspect when considering buffet aerodynamics is given by the reproduction of the self-sustained unsteadiness of the buffeting flow. Even without any external excitation, the flow is characterized by large shock-boundary layer interaction, resulting in shock movement and flow separation. Finally, the performance of the trained network is demonstrated by predicting the aerodynamic loads of the NACA0012 airfoil considered at transonic freestream conditions. Therefore, the airfoil is excited by a forced pitching motion beyond the buffet-critical angle of attack. A comparison with a full-order computational fluid dynamics (CFD) solution shows that the essential characteristics of the nonlinear buffet phenomenon are captured by the ROM method.

在当前的工作中，基于长短期记忆（LSTM）神经网络的降阶建模（ROM）框架被应用于跨音速自助餐空气动力学的预测。这种类型的网络具有对顺序数据进行建模的巨大潜力，这对于捕获与不稳定空气动力学相关的时间延迟效应非常有利。因此，提出了非线性识别程序以及所得ROM的一般化。此外，执行基于蒙特卡洛的训练程序以便估计统计误差。ROM的训练数据集是通过强制运动的非稳态雷诺平均Navier Stokes（URANS）模拟提供的。在培训过程之后，ROM用于计算时变积分量，例如空气动力和力矩系数。考虑到自助餐空气动力学方面最具挑战性的方面是自助餐流动的自持不稳定性的再现。即使没有任何外部激励，该流的特征也在于较大的激波边界层相互作用，从而导致激波运动和流分离。最后，通过预测在跨音速自由流条件下考虑的NACA0012机翼的空气动力学负载，可以证明训练网络的性能。因此，翼型被超过俯仰临界攻角的强制俯仰运动所激发。