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Fractional-Derivative Maxwell Kelvin Model for '5+4' Viscoelastic Damping Wall Subjected to Large Deformation

机译:大变形下“ 5 + 4”粘弹性阻尼墙的分数阶导数麦克斯韦·开尔文模型

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摘要

Considering the larger vibration amplitude and several viscoelastic material layers, a fractional-derivative Maxwell Kelvin (FDMK) viscoelastic mechanical model is proposed for "5+4" viscoelastic damping wall, which is used for vibration control of building structures. The development of the model is based on in-parallel combination of fractional Maxwell model and fractional Kelvin model. The proposed model is experimentally validated and very good agreement between predicted and experimental results was obtained. The results confirm that the FDMK model is accurate in simulating the hysteresis properties of the "5+4" viscoelastic damping wall under large deformation. From the areas of the experimental and theoretical hysteresis loops, under 300% strain, the predicted result is the most accurate in prediction of the energy dissipation and the second is the prediction under 450% strain. Moreover, from the comparisons of dynamic properties (storagemodulus, lossmodulus, etc.), the FDMK model works satisfactorily. The FDMK model is more sensitive in energy dissipation than in energy storage.
机译:考虑到较大的振动幅度和几层粘弹性材料层,提出了分数阶导数麦克斯韦开尔文(FDMK)粘弹性力学模型用于“ 5 + 4”粘弹性阻尼墙,用于建筑结构的振动控制。该模型的开发基于分数麦克斯韦模型和分数开尔文模型的并行组合。该模型经过实验验证,并在预测结果与实验结果之间取得了很好的一致性。结果证实,FDMK模型在大变形下模拟“ 5 + 4”粘弹性阻尼墙的滞后特性方面是准确的。从实验和理论上的磁滞回线区域来看,在300%应变下,预测结果是最准确的能量耗散预测,第二个是在450%应变下的预测。此外,通过比较动态特性(存储模量,损耗模量等),FDMK模型可以令人满意地工作。与能量存储相比,FDMK模型在能量耗散方面更为敏感。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第6期|3170967.1-3170967.11|共11页
  • 作者

    Xu Junhong; Li Aiqun; Shen Yang;

  • 作者单位

    Southeast Univ, Sch Civil Engn, Nanjing 210096, Jiangsu, Peoples R China;

    Southeast Univ, Sch Civil Engn, Nanjing 210096, Jiangsu, Peoples R China|Beijing Univ Civil Engn & Architecture, Beijing 100044, Peoples R China;

    Hohai Univ, Key Lab Minist Educ Geomech & Embankment Engn, Nanjing 210098, Jiangsu, Peoples R China;

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