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MISTUNING IDENTIFICATION AND MODEL UPDATING OF AN INDUSTRIAL BLISK

机译:迷信的识别和模型更新工业眨眼

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The results of a complete study of mistuning identification on an industrial blisk are presented. The identification method used here is based on a model-updating technique of a reduced-order model where measured modal data are taken as input. This reduced-order model is build using component mode synthesis and mistuning is introduced as perturbations of the cantilevered-blade modes. The measured modal data are extracted from global measurements of the blisk's forced response. As we use a one point excitation, this measurement procedure allows the acquisition of the all modes of a given family with a quite simple experimental setup. A selection of the best identified modal data is finally performed. During the mistuning identification procedure, these measured data are regularized using an eigenvector assignment technique which reduces the influence of eventual measurement errors. An inverse problem is defined based on the perturbed (mistuned) modal equation, with measured modes as input and mistuning parameters as unknown. Then, the reduced-order model is updated with the identified mistuning, we first perform a correlation on modal responses (using eigenfrequency deviation criteria and MACs). Finally, correlation results on forced responses are presented and discussed.
机译:介绍了在工业眨眼系上完全研究迷信鉴定的结果。这里使用的识别方法是基于模型 - 更新技术,其减少阶模型,其中测量的模态数据被视为输入。使用组件模式合成和雾化作为悬臂式模式的扰动来构建该阶阶模型。测量的模态数据从闪烁的强制响应的全局测量中提取。当我们使用一个点激励时,该测量过程允许使用相当简单的实验设置获取给定系列的所有模式。最终执行最佳识别的模态数据。在迷信识别过程中,使用特征向量分配技术来规范这些测量数据,这降低了最终测量误差的影响。基于扰动(迷雾的)模态方程来定义逆问题,其中测量模式作为输入和雾化参数作为未知。然后,通过识别的迷信更新缩小阶模型,我们首先对模态响应进行相关性(使用特征频率偏差标准和MAC)。最后,提出和讨论了强制响应的相关结果。

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