西南交通大学学报2025，Vol.60Issue(3)：599-607,9.DOI:10.3969/j.issn.0258-2724.20230261
基于自编码器的非线性气动力辨识及非线性颤振分析
Nonlinear Aerodynamic Force Identification and Nonlinear Flutter Analysis Based on Autoencoder
摘要
Abstract
In order to identify the nonlinear aerodynamic forces and calculate nonlinear flutters of a nonlinear dynamic system,an autoencoder model based on the neural network method and numerical solution of motion equation was proposed.The 5∶1 rectangular cross-section was taken as the research object.Through free vibration wind tunnel tests of the sectional model,the amplitude dependence of the nonlinear damping and the steady-state amplitude responses of the nonlinear flutter of the system were tested,and it was clarified that the tested cross-section had the only steady-state flutter response at different reduced wind speeds.Based on the experimental data,the proposed autoencoder model was trained.The nonlinear aerodynamic force encoder model that accurately described displacement and speed was obtained to realize motion time-history analysis of the nonlinear flutter of the 5∶1 rectangular cross-section under different dynamic parameters.Research results show that the proposed autoencoder model can accurately identify the nonlinear aerodynamic force time-history containing high-order harmonic components only by relying on a free vibration wind tunnel test without the need to carry out force or pressure tests;the proposed model can accurately reproduce the motion time-history of nonlinear flutter under different initial conditions and the steady-state amplitude responses at different reduced wind speeds.The maximum error of torsional steady-state amplitude is less than 5%,and the average error is 1.15%.It has high extensibility and can provide a reference for subsequent related research.关键词
非线性颤振/风洞试验/神经网络/编码器/解码器Key words
nonlinear flutter/wind tunnel test/neural network/encoder/decoder分类
计算机与自动化引用本文复制引用
梅瀚雨,廖海黎,王昌将..基于自编码器的非线性气动力辨识及非线性颤振分析[J].西南交通大学学报,2025,60(3):599-607,9.基金项目
国家自然科学基金项目(51778547) （51778547）
浙江省自然科学基金项目(LQN25E080012) （LQN25E080012）
