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首页> 外文会议>International Congress on Sound and Vibration >AN IMPROVED METHOD FOR MODEL UPDATING PARAMETER SELECTION AND ITS APPLICATIONS
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AN IMPROVED METHOD FOR MODEL UPDATING PARAMETER SELECTION AND ITS APPLICATIONS

机译：一种改进的模型更新参数选择方法及其应用方法

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摘要

The analytical predictions from a finite element model often differ from the experimental results of a target structure. Finite element model updating is an inverse problem to identify and correct uncertain modeling parameters that leads to better predictions of the dynamic behavior of the structure. Unlike other inverse problems, the restrictions on the selection and variation of the parameters are severe. The updating parameter selection should be made with the aim of correcting uncertainties in the model. Moreover, the criteria or objective functions which designate differences between analytical and experimental results need to be sensitive to such selected parameters. Otherwise, the parameters should deviate far from their initial values and lose their physical foundation in order to get acceptable correlations between analytical and test results. To avoid an ill-conditioned numerical problem, the number of parameters should be kept as small as possible. Thus, the parameter selection requires considerable physical insight into the target structure, and trial-and-error approaches are commonly used. In this work, the importance of updating parameters is illustrated through a case study and a method to guide parameter selection is suggested. After assigning an updating parameter to each of the finite elements with modeling errors, this method iteratively reduces the number of the parameters through grouping neighboring parameters while maintaining the minimum sacrifice of sensitivity. The usefulness of the suggested method is proved by both a simulated case study and a real engineering application.
机译：来自有限元模型的分析预测通常与目标结构的实验结果不同。有限元模型更新是识别和纠正不确定建模参数的逆问题，导致更好地预测结构的动态行为。与其他逆问题不同，对参数的选择和变化的限制是严重的。应采用更新参数选择，以纠正模型中的不确定性。此外，指定分析和实验结果之间的差异的标准或客观函数需要对这种所选参数敏感。否则，参数应远离其初始值并失去其物理基础，以便在分析和测试结果之间获得可接受的相关性。为避免不良的数值问题，应尽可能小的参数数量。因此，参数选择需要相当大的物理洞察到目标结构，并且通常使用试验和误差方法。在这项工作中，通过案例研究和提出指导参数选择的方法来说明更新参数的重要性。在通过建模错误为每个有限元分配更新参数之后，该方法通过分组相邻参数来迭代地减少参数的数量，同时保持灵敏度的最小牺牲。通过模拟案例研究和实际工程应用证明了建议方法的有用性。

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