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Application of a long short-term memory neural network for modeling transonic buffet aerodynamics

机译：长短期内存神经网络在跨音频自助式空气动力学建模的应用

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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. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：在本作工作中，基于长短期存储器（LSTM）神经网络的阶阶建模（ROM）框架用于预测跨音速自助式空气动力学。这种类型的网络具有高潜力，用于建模顺序数据，这有利于捕获与不稳定的空气动力学相关的延时效果。因此，提出了非线性识别过程以及所得ROM的泛化。此外，以估计统计误差来执行基于蒙特卡罗的训练程序。 ROM的训练数据由强制运动不稳态雷诺瓦尔平均Navier Stokes（Urans）仿真提供。在训练过程之后，rom被应用于计算时变的整数量，例如空气动力力和矩系数。在考虑自助式空气动力学时，最具挑战性的方面是通过再现自持续的缓冲流动的不稳定性。即使没有任何外部激励，流量也具有大的冲击边界层相互作用，导致冲击运动和流动分离。最后，通过预测在跨音自由流动条件下考虑的NaCA0012翼型的空气动力学载荷来证明训练网络的性能。因此，翼型通过强制俯仰运动来激发超出自助式临界角度的强制倾斜运动。与全阶计算流体动力学（CFD）解决方案的比较表明，非线性自助式现象的基本特性由ROM方法捕获。 （c）2021 Elsevier Masson SAS。版权所有。

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