In the present work, an efficient Reduced-Order Model is developed for the prediction of motion-induced unsteady pressure distributions. The model is trained on the basis of synthetic data generated by full-order Computational Fluid Dynamics (CFD) simulations. The nonlinear identification task is to predict a snapshot representing the pressure distribution for the current time step based on respective snapshots of previous time steps and applied excitation. Once a Reduced-Order Model is conditioned on training data, it can predict sequences of the pressure distribution in a recurrent manner based on the excitation signal. Hence, it is able to capture the motion-induced nonlinear unsteady aerodynamics for a given configuration at fixed free-stream conditions. In this way, computationally extensive CFD simulations can be substituted by the application of the more efficient Reduced-Order Model. The nonlinear behavior of the aerodynamic system is captured based on a deep convolutional neural network. The performance of the Reduced-Order Model is demonstrated based on the LANN (Lockheed-Georgia, Air Force Flight Dynamics Laboratory, NASA-Langley and NLR) wing performing high-amplitude pitching motion in transonic flow. The unsteady aerodynamics of the considered test case is dominated by nonlinear effects due to complex moving shock structures both on the upper and lower surface of the wing. The Reduced-Order Model yields a superior prediction accuracy at a speed-up of more than three orders of magnitude compared to the employed CFD method.

在目前的工作中，有效的降序模型被开发用于预测运动引起的非稳态压力分布。该模型是基于通过全阶计算流体动力学（CFD）模拟生成的合成数据进行训练的。非线性识别任务是基于先前时间步长和应用的激励分别预测代表当前时间步长的压力分布的快照。一旦降阶模型以训练数据为条件，它就可以基于激励信号以递归方式预测压力分布的顺序。因此，它能够在固定的自由流条件下捕获给定配置的运动引起的非线性非稳态空气动力学。这样，计算广泛的CFD模拟可以由更有效的降阶模型代替。基于深度卷积神经网络，捕获了气动系统的非线性行为。降阶模型的性能基于LANN（大号ockheed-佐治亚州，阿IR力飞行动力学实验室，Ñ ASA-兰利和Ñ LR）在跨音速流动翼进行高振幅俯仰运动。由于机翼的上，下表面均具有复杂的运动冲击结构，因此所考虑的测试用例的不稳定空气动力学受到非线性效应的支配。与采用的CFD方法相比，降阶模型在超过三个数量级的加速时可产生出众的预测精度。