Efficient prediction of transonic flutter boundaries for varying Mach number and angle of attack via LSTM network

Li Wencheng; Gao Xiumin; Liu Haojie

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Efficient prediction of transonic flutter boundaries for varying Mach number and angle of attack via LSTM network

机译：通过LSTM网络对不同马赫数和攻角的跨音颤动边界的高效预测

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Transonic aeroelastic analysis can be carried out accurately and efficiently by using the aerodynamic Reduced-Order Modeling (ROM) approach. However, the efficiency and generalization capability of traditional time-dependent ROM should be further enhanced, especially when dealing with the case for varying flight parameters. For such a purpose, a set of flight samples for different Mach numbers and mean angles of attack in transonic regime are selected to cover the concerned parameter space. Subsequently, a typical filtered white Gaussian noise is used as the input signal to excite the dynamical behavior of the aerodynamic system via the direct Computational Fluid Dynamic (CFD) technique, and the corresponding input and output data at all the flight samples are used as the training data set. Afterwards, based on the CFD training data set, the dynamical relationship between aerodynamic output and displacement input for varying Mach number and mean angle of attack can be approximately fitted by using the Long Short Term Memory (LSTM) network, which is a time-series prediction approach of deep learning method. Finally, the transonic flutter boundaries of a NACA 64A010 airfoil are investigated to assess the validity of the proposed approach. The comparison with CFD results shows that, the ROM can predict the unsteady aerodynamic response and aeroelastic characteristics well with low computation cost. In particular, the flutter boundaries of the concerned airfoil at different Mach numbers and mean angles of attack are obtained, due to the absence of time-delay term in surrogate model, the generalization capacity and modeling efficiency of the ROM are improved. (C) 2020 Elsevier Masson SAS. All rights reserved.

机译：通过使用空气动力学降低的阶型建模（ROM）方法，可以准确且有效地进行肿仓空气弹性分析。然而，应进一步增强传统时间依赖性ROM的效率和泛化能力，特别是在处理不同飞行参数的情况下。出于这种目的，选择用于不同马赫数的飞行样本和跨音速状态的平均攻击角度以覆盖有关参数空间。随后，使用典型的过滤的白色高斯噪声作为输入信号，以通过直接计算流体动态（CFD）技术激发空气动力系统的动态行为，以及所有飞行样本的相应输入和输出数据用作培训数据集。之后，基于CFD训练数据集，通过使用长短短期存储器（LSTM）网络，可以大致拟安装用于变化的Mach数量和平均攻角的空气动力输出和位移输入之间的动态关系，这是一个时间序列深度学习方法的预测方法。最后，研究了NaCA 64a010翼型的跨音颤动边界，以评估所提出的方法的有效性。与CFD结果的比较表明，ROM可以通过低计算成本预测不稳定的空气动力学响应和空气弹性特性。特别地，由于替代模型中的不存在时间延迟期，因此获得了不同马赫数的翼型翼型的颤动边界和平均攻击的平均角度，改善了ROM的泛化容量和建模效率。（c）2020 Elsevier Masson SAS。版权所有。

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来源
《Aerospace science and technology》 |2021年第3期|106451.1-106451.12|共12页
作者
Li Wencheng; Gao Xiumin; Liu Haojie;
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作者单位

Huazhong Agr Univ Coll Engn Wuhan 430070 Peoples R China|Minist Agr & Rural Affairs Key Lab Agr Equipment Midlower Yangzi River Wuhan 430070 Peoples R China;

Nanjing Tech Univ Sch Phys & Math Sci Nanjing 211816 Peoples R China;

Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Reduced-order modeling; Deep learning; Transonic flow; Unsteady response; Critical flutter speed;

机译：减少阶型建模;深入学习;跨音流动;不稳定的响应;临界颤动速度;

Efficient prediction of transonic flutter boundaries for varying Mach number and angle of attack via LSTM network

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