一种基于SNM降阶技术的整体叶盘结构失谐识别方法研究

本文以谐调叶盘有限元模型解析模态和失谐系统的实验测量模态作为基础信息，提出了一种整体叶盘结构失谐识别方法。该方法基于SNM降阶技术，大大降低了识别过程的计算花费以及对基础信息的要求；采用了子矩阵型技术使得失谐参数定义更加的自由，并使得该方法具有模型修正的功能；利用最可能向量技术处理实验测量模态振型，有效的限制了测量噪声、非线性等因素对识别过程的影响。最后以一个真实叶盘结构的数值仿真分析证明了该方法的正确及有效性。

Mistuning identification for integrally bladed disk based on the SNM reduced technique

This paper presents a method of mistuning identification for integrally bladed disk. This method takes the analytical modes obtained by FEM of tuned bladed disk and the measured modes of mistuned experimental bladed disk as basic information. The identification method is based on the SNM (Subset of Nominal Modes) reduced technique, which greatly lowers the computational costs and required information of the identification procedure. The submatrix method makes the definition of mistuning parameters more freely and adds the model modification function to this identification method. The measured data is filtered through the best achievable eigenvectors technique, which effectively suppresses the influence of measurements noise and non-linearity on the identification procedure. Finally, numerical simulation of an industrial bladed disk is preformed to validate the effectiveness of the identification method.