【摘要】 提出将贝叶斯统计推断方法推广应用于大气紊流激励下飞行器结构的颤振分析，对含不确定性因素影响的模态参数识别与颤振边界预测进行研究。在采用自然激励技术从结构在大气紊流激励下的响应中提取自由衰减信号后，基于贝叶斯统计推断，通过马尔科夫链蒙特卡罗(Markov chain Monte Carlo, MCMC)算法对结构模态参数的后验概率密度函数进行采样识别，并利用Z-W(Zimmerman-Weissenburger)颤振裕度法获取颤振速度概率分布，预测颤振边界并分析其不确定性。进行了数值仿真研究，对大气紊流激励下的结构响应数据进行分析，验证了所提出方法的有效性。更多还原

【Abstract】 An extended Bayesian statistical inference method was proposed for flutter analysis for aircraft structures under excitation of atmospheric turbulence to consider the uncertainties in modal parameter identification and flutter boundary prediction. After the free decay responses were extracted from the structural responses under the excitation of atmospheric turbulence by natural excitation technique, based on Bayesian statistical inference, the posterior probability density functions of structural modal parameters were sampled and identified by the Markov chain Monte Carlo(MCMC) algorithm. Then the probability density distribution for flutter velocity was obtained by the Z-W(Zimmerman-Weissenburger) flutter margin method to predict the flutter boundary and analyze the associated uncertainties. Numerical simulation studies were carried out to analyze the structural response data excited by atmospheric turbulence, and the effectiveness of the proposed method was verified.更多还原