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Model updating using the closed-loop modal data: application to indeterminate structure

机译:模型使用闭环模态数据更新:应用于不确定结构

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The parameter modification of the initial finite element model (FEM) to match its modal data with the experimental ones requires the modal sensitivity matrix. There are two cases in which this method can't be applied successfully; the deficiency of the necessary modal information and the ill-conditioning of the modal sensitivity matrix. In this research, a novel concept that introduces the feedback loop from the sensors to the exciter of the conventional modal test setup is proposed. The research is focused on the ill-conditioning problem. Ill-conditioning happens by the similar modal sensitivities of some parameters. The feedback loop can alter the effect of the parameter to the system response. That means the modal sensitivity of the parameters becomes different from the original ones by the feedback loop. Through the proper use of feedback loop, we can change the similar modal sensitivities of some updating parameters and consequently update the system parameters successfully, which is impossible with the conventional open loop approach because of the ill-conditioned sensitivity matrix.
机译:初始有限元模型(FEM)的参数修改以将其模态数据与实验结果匹配需要模态灵敏度矩阵。有两种情况下,这种方法无法成功应用;缺乏必要的模态信息和模态灵敏度矩阵的不良状态。在该研究中,提出了一种新颖的概念,其将来自传感器的反馈回路引入到传统模态测试设置的激励器中。该研究专注于不良状态问题。某些参数的类似模态敏感性发生了不良状态。反馈循环可以改变参数对系统响应的影响。这意味着参数的模态敏感度由反馈循环与原始的模态灵敏度不同。通过正确使用反馈循环,我们可以改变一些更新参数的类似模态敏感性,从而成功更新系统参数,因为常规开环方法是不可能的,因为矩阵不明的灵敏度矩阵。

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