In this paper, a reduced-order model (ROM) based on data-driven machine learning algorithm is constructed to identify the aerodynamic forces of airfoil undergoing large-amplitude pitching oscillation. Strong nonlinearity and unsteadiness in aerodynamics is a major challenge in the prediction of aerodynamic forces. To deal with this problem, the recurrent neural network (RNN) with gated recurrent unit (GRU) is applied for nonlinear and unsteady aerodynamic identification. A motion input signal which covers a wide range of frequency and amplitude is designed to enable the ROM with generalization capability. Shear stress transport (SST) model with low-Reynolds number modification is introduced into the computational fluid dynamics (CFD) method to calculate the aerodynamic forces as the training data. The time step size and lag order of the model are determined by the frequency domain characteristics of the training data. The results suggest that the proposed ROM has a high identification precision on nonlinear unsteady aerodynamics. The well-trained ROM could accurately predict the aerodynamic forces of airfoil undergoing sinusoidal oscillations with various frequencies and amplitudes. The proposed ROM shows advantages in accuracy over other ROM techniques. The calculation speed of ROM is 69 times faster than that of CFD method on the premise of accuracy, which can be expected a good application in engineering.

中文翻译：

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基于门控循环单元网络的大振幅俯仰振荡翼型非定常非线性气动预测

本文构建了一种基于数据驱动机器学习算法的降阶模型（ROM），用于识别大振幅俯仰振荡的翼型气动力。空气动力学中的强非线性和不稳定是预测空气动力的主要挑战。为了解决这个问题，具有门控循环单元（GRU）的循环神经网络（RNN）被应用于非线性和非定常空气动力学识别。设计了覆盖广泛频率和幅度的运动输入信号，以使 ROM 具有泛化能力。将具有低雷诺数修正的剪应力传递 (SST) 模型引入计算流体动力学 (CFD) 方法中，以计算气动力作为训练数据。模型的时间步长和滞后阶数由训练数据的频域特征决定。结果表明，所提出的ROM对非线性非定常空气动力学具有较高的识别精度。训练有素的 ROM 可以准确地预测机翼的空气动力，该机翼正经历各种频率和幅度的正弦振荡。与其他 ROM 技术相比，所提出的 ROM 在精度方面具有优势。ROM的计算速度在精度前提下比CFD方法快69倍，可以期待在工程中得到很好的应用。训练有素的 ROM 可以准确地预测机翼的空气动力，该机翼正经历各种频率和幅度的正弦振荡。与其他 ROM 技术相比，所提出的 ROM 在精度方面具有优势。ROM的计算速度在精度前提下比CFD方法快69倍，可以期待在工程中得到很好的应用。训练有素的 ROM 可以准确地预测机翼的空气动力，该机翼正经历各种频率和幅度的正弦振荡。与其他 ROM 技术相比，所提出的 ROM 在精度方面具有优势。ROM的计算速度在精度前提下比CFD方法快69倍，可以期待在工程中得到很好的应用。