Guo Qing, Li Xiaoqiang, Zhou Zhijie, Ma Dexiao, Wang Yuzhuo
School of Aeronautics, Northwestern Polytechnical University, Xi'an, 710072, China.
National Key Laboratory of Aircraft Configuration Design, Xi'an, 710072, China.
Sci Rep. 2025 Jan 3;15(1):623. doi: 10.1038/s41598-024-82573-7.
Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results.
颤振在空气动力学和飞机设计领域都是极为重要的学术课题。由于颤振会引发包括分岔、倍周期以及混沌等多种现象，它成为了最不可预测的不稳定现象之一。通过研究系统辨识技术预测颤振速度的前景，高柔韧性机翼气动弹性建模的复杂性将得到大幅简化。因此，提出了一种基于新型神经网络（NN）的气动弹性系统辨识方法。所提出的基于神经网络的方法构建了具有不同材料和尺寸的高柔韧性机翼颤振模型的神经网络框架，该框架能够有效预测柔性机翼的颤振速度。通过与仿真结果对比，验证了该方法的准确性。
