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首页> 外文期刊>Mechanical systems and signal processing >An improved updating parameter selection method and finite element model update using multiobjective optimisation technique
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An improved updating parameter selection method and finite element model update using multiobjective optimisation technique

机译：一种改进的更新参数选择方法和使用多目标优化技术的有限元模型更新

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摘要

Finite element model updating is a procedure to minimise the differences between analytical and experimental results and is usually posed as an optimisation problem. In model updating process, one requires not only satisfactory correlations between analytical and experimental results, but also maintaining physical significance of updated parameters. For this purpose, setting up of an objective function and selecting updating parameters are crucial steps in model updating. These require considerable physical insight and usually trial-and-error approaches are common to use. In conventional model updating procedures, an objective function is set as the weighted sum of the differences between analytical and experimental results. But the selection of the weighting factors is not clear since the relative importance among them is not obvious but specific for each problem. In this work, multiobjective optimisation technique is introduced to extremise several objective terms simultaneously. Also the success of finite element model updating depends heavily on the selection of updating parameters. In order to avoid an ill-conditioned numerical problem, the number of updating parameters should be kept as small as possible. Such parameters should be selected with the aim of correcting modelling errors and modal properties of interest should be sensitive to them. When the selected parameters are inadequate, then the updated model becomes unsatisfactory or unrealistic. An improved method to guide the parameter selection is suggested.
机译：有限元模型更新是使分析结果与实验结果之间的差异最小化的过程，通常被视为优化问题。在模型更新过程中，不仅需要令人满意的分析结果与实验结果之间的相关性，而且还需要保持更新参数的物理意义。为此，建立目标函数和选择更新参数是模型更新的关键步骤。这些需要大量的物理见识，通常使用反复试验的方法。在传统的模型更新过程中，目标函数设置为分析结果与实验结果之间差异的加权和。但是，加权因子的选择尚不明确，因为它们之间的相对重要性并不明显，而是针对每个问题。在这项工作中，引入了多目标优化技术以同时去除多个目标项。同样，有限元模型更新的成功很大程度上取决于更新参数的选择。为了避免不良的数值问题，更新参数的数量应保持尽可能小。选择这些参数的目的是纠正建模误差，并且所关注的模态特性应对其敏感。当所选参数不足时，更新后的模型将变得不令人满意或不切实际。提出了一种指导参数选择的改进方法。

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