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Dynamics Properties of a Go-Kart Chassis Structure and Its Prediction Improvement Using Model Updating Approach

机译:卡丁车底盘结构的动力学特性及其使用模型更新方法的预测改进

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摘要

Model updating is concerned about the correction of finite element models by processing the record of dynamic response from test structures in order to have an accurate model for any simulated analysis. Finite element model updating had emerged years ago as an important subject in structural dynamics. It has been used frequently and has been successfully applied to many fields especially in detecting the dynamic stiffness of a structure. The purpose of this study is to perform model updating of a go-kart chassis structure in order to reduce the percentage of error between the experimental modal analysis (EMA) and finite element analysis (FEA). Modal properties (natural frequency, mode shapes, and damping ratio) of the go-kart chassis structure were determined using both EMA and FEA. Correlation of the modal parameters gathered in FEA and EMA was carried out before optimizing the data from finite element. By adjusting the selective parameters, incongruities between those two analyses are generally reduced. The sensitivity of selected parameters is also obtained. The significant reduction in percentageof error before and after model updating procedure was carried out in this study clearly shows that model updating technique is a reliable method in reducing the discrepancies between EMA and FEA. Therefore, in cases of high discrepancies between analytical and actual test data, model updating can be considered as an option in order to obtain better correlation between those two sets of data.
机译:模型更新关注通过处理来自测试结构的动态响应记录来对有限元模型进行校正,以便为任何模拟分析提供准确的模型。有限元模型更新已在多年前成为结构动力学的重要课题。它已被频繁使用,并且已成功应用于许多领域,尤其是在检测结构的动态刚度方面。这项研究的目的是对卡丁车底盘结构进行模型更新,以减少实验模态分析(EMA)和有限元分析(FEA)之间的误差百分比。使用EMA和FEA确定了卡丁车底盘结构的模态特性(固有频率,振型和阻尼比)。在优化有限元数据之前,先进行了在FEA和EMA中收集的模态参数的相关性。通过调整选择参数,通常可以减少这两个分析之间的不一致性。还可以获得所选参数的灵敏度。在这项研究中,在进行模型更新之前和之后,误差百分比的显着降低清楚地表明,模型更新技术是减少EMA与FEA之间差异的可靠方法。因此,在分析数据与实际测试数据之间存在高度差异的情况下,可以将模型更新视为一种选择,以便在这两组数据之间获得更好的相关性。

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