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席梓严, 戴玉婷, 黄广靖, 杨超. 基于深度算子神经网络的翼型失速颤振预测. 力学学报, 2024, 56(3): 1-9. DOI: 10.6052/0459-1879-23-522
引用本文: 席梓严, 戴玉婷, 黄广靖, 杨超. 基于深度算子神经网络的翼型失速颤振预测. 力学学报, 2024, 56(3): 1-9. DOI: 10.6052/0459-1879-23-522
Xi Ziyan, Dai Yuting, Huang Guangjing, Yang Chao. Airfoil stall flutter prediction based on DeepONet. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(3): 1-9. DOI: 10.6052/0459-1879-23-522
Citation: Xi Ziyan, Dai Yuting, Huang Guangjing, Yang Chao. Airfoil stall flutter prediction based on DeepONet. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(3): 1-9. DOI: 10.6052/0459-1879-23-522

基于深度算子神经网络的翼型失速颤振预测

AIRFOIL STALL FLUTTER PREDICTION BASED ON DEEPONET

  • 摘要: 失速颤振是弹性结构大幅俯仰振动与动态失速气动力耦合所发生的一种单自由度失稳现象, 需有效预测其失稳分岔速度与失稳后的极限环振荡幅值. 针对NACA0012翼型大幅俯仰运动气动力预测问题, 发展了由嵌入门限循环单元或长短时记忆神经网络单元的分支网络(branch net)和主干网络(trunk net)组成的深度算子神经网络(deep operator network, DeepONet)结构. 通过给定大幅俯仰运动下的动态失速CFD气动力数据对深度算子神经网络参数进行训练, 建立了高精度动态失速气动力的数据驱动模型, 并有效预测其他俯仰运动下的非定常气动力. 更进一步, 将基于深度算子神经网络的非定常气动力数据驱动模型与结构动力学方程耦合, 采用数值积分方法预测失速颤振的失稳分岔速度和不同速度下的极限环振荡特性. 结果表明, 在动态失速气动力预测精度方面, 与普通循环神经网络相比, 深度算子神经网络通过引入主干网络结构, 可考虑运动与气动力间的迟滞特性, 气动力预测平均绝对误差降低2%, 误差分散性更低; 在失速颤振预测方面, 极限环振荡幅值误差在2%以内, 增加来流速度输入的深度算子神经网络模型预测误差显著小于固定速度输入的算子模型.

     

    Abstract: Stall flutter is a single degree-of-freedom instability phenomenon that occurs due to the coupling of large pitch motion of elastic structures and dynamic stall aerodynamic forces. It is necessary to effectively predict its bifurcation speed and the limit cycle amplitude. To address the problem of predicting the aerodynamic forces during large pitch oscillations of the NACA0012 airfoil, a deep operator network (DeepONet) structure was developed, consisting of a branch net with embedded gated recurrent units or long-short-term memory neural network units and a trunk net. The DeepONet structure was trained using dynamic stall CFD aerodynamic force data for large pitch oscillations, and a high-fidelity data-driven model for dynamic stall aerodynamic forces was established, which effectively predicted unsteady aerodynamic forces for other pitch oscillations. Furthermore, the data-driven model for unsteady aerodynamic forces based on the DeepONet was coupled with the structural dynamics equation, and numerical integration was used to predict the bifurcation speed of stall flutter and the limit cycle oscillation characteristics at different speeds. The results showed that, compared with ordinary recurrent neural networks, the DeepONet could consider the hysteresis characteristics between motion and aerodynamic forces by introducing a trunk net structure, resulting in a 2% reduction in the mean absolute error in predicting aerodynamic forces during dynamic stall. Regarding the prediction of stall flutter, the error in the limit cycle oscillation amplitude was within 2%, and the DeepONet model with inflow velocity input had significantly smaller prediction errors than the operator model without velocity input.

     

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