Reduced thin-layer elements for modeling the nonlinear transfer behavior of bolted joints of automotive engine structures
Reduced thin-layer elements for modeling the nonlinear transfer behavior of bolted joints of automotive engine structures
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DOI：
10.1007/s00419-015-1109-1
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发表时间：
2016-01
影响因子：
2.8
通讯作者：
C. Ehrlich;A. Schmidt;L. Gaul
中科院分区：
文献类型：
--
作者：
C. Ehrlich;A. Schmidt;L. Gaul
装配式结构的减振性能在很大程度上受节点间摩擦阻尼的影响。因此，在建模过程中必须考虑这些影响。在力学界面上应用具有线性、各向异性材料模型的薄层单元(TLE)来考虑节点的阻尼，与前人的实验模态分析显示出很好的一致性。在TLE模型中，假定了恒定的滞回阻尼。在一个孤立搭接接头上进行了TLE的阻尼和刚度参数的实验识别。通过模型修正或不确定性分析来解决模型简化和参数不确定性带来的不精确问题。这需要对模型进行多次评估，这些模型在所有方面都是等价的，但它们的TLE参数化。在这项工作中，提出了一种TLE建模方法的模型降阶技术，该技术显著降低了关节参数改变后重新计算特征值的计算量。该约简基于本征敏感度分析，并为每个本征值产生一个单一的线性方程。将该方法应用于一个模型修正实例。在这里，模型简化允许更多的设计变量，这意味着使用物理上更有意义的模型可以更准确地再现实验数据。
Damping properties of assembled structures are largely influenced by frictional damping between joint interfaces. Therefore, these effects must be considered during the modeling process. Applying thin-layer elements (TLEs) with a linear, orthotropic material model on mechanical interfaces to incorporate joint damping has shown good agreement with experimental modal analysis in previous work. In the TLE model, constant hysteretic damping is assumed. The damping and stiffness parameters for the TLEs are experimentally identified on an isolated lap joint. Imprecisions caused by model simplifications and parameter uncertainty are addressed by model updating or uncertainty analysis. This requires multiple evaluations of models that are equivalent in all respects but their TLE parameterization. In this work, a model reduction technique for the TLE modeling approach is presented which significantly reduces computational cost for the re-calculation of eigenvalues after joint parameters are changed. The reduction is based on an eigensensitivity analysis and results in a single, linear equation for each eigenvalue. The presented approach is applied to a model updating example. Here, the model reduction allows for a much larger number of design variables which means experimental data can be reproduced more accurately with a physically more meaningful model.