Stall flutter prediction based on multi-layer GRU neural network  

作　　者：Yuting DAI Haoran RONG You WU Chao YANG Yuntao XU 

机构地区：[1]School of Aeronautic Science and Engineering,Beihang University,Beijing 100083,China

出　　处：《Chinese Journal of Aeronautics》2023年第1期75-90,共16页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.11672018).

摘　　要：The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit(GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation(LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.

关 键 词：Deep learning Dynamic stall Limit-cycle oscillation Reduced order model Stall flutter 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]