摘要
涂层整体叶盘失谐参数辨识是研究整体叶盘涂层减振设计的重要基础,基于构件模态失谐模型(CMM)提出了一种涂层整体叶盘失谐辨识方法。在简要介绍该失谐模型的基础上,根据涂层整体叶盘复合结构的特点,进行了质量失谐与刚度失谐辨识方法的理论推导,其中对质量失谐的辨识方法进行了近似处理;给出了失谐辨识的操作流程,包括质量及刚度失谐模态参与因子计算、质量失谐辨识、求解正则坐标向量和刚度失谐辨识等4个步骤;按照所提出的辨识流程和算法对一个简化的涂层整体叶盘进行了失谐辨识实践,得到了该叶盘质量和刚度失谐分布。按照辨识结果重新建立了一个新的失谐整体叶盘分析模型,通过对比由该模型与原涂层整体叶盘有限元模型获得的叶盘固有特性证明了辨识结果的合理性。
The mistuning parameter identification of a coating blisk is an important foundation for its vibration reduction design.Based on the component mode mistuning(CMM) model,a method for the mistuning identification of the coating blisk was presented.On the basis of the introduction of the CMM,the theoretical derivation for the identification of mass mistuning and stiffness mistuning was carried out according to the composite structure characteristics of the coating blisk,in which the identification of mass mistuning was made with an approximate treatment.The procedure of the mistuning identification was provided,including the modal participation factors calculations of mass mistuning and stiffness mistuning,mass mistuning identification,calculation of normal coordinate vector and stiffness mistuning identification.With the proposed identification procedure and algorithm,the mistuning identification of a simplified coating blisk was performed,and the distributions of mass and stiffness mistuning of blisk were obtained.Based on the identification results,a new mistuning blisk model was set up and the rationality of the identification results were proved by comparing the natural characteristics obtained by the new model and the finite element model of the whole coating blisk.
作者
徐昆鹏
孙伟
高俊男
梅雪峰
XU Kunpeng;SUN Wei;GAO Junnan;MEI Xuefeng(School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China;Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China,Northeastern University,Shenyang 110819,China;Guidaojiaotong Polytechnic Institute,Shenyang 110027,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2019年第14期118-124,共7页
Journal of Vibration and Shock
基金
国家自然科学基金(51775092)
