Natural frequencies-based stochastic finite element model updating using equal weighting factors

IF 0.7 Q4 ENGINEERING, CIVIL
Asier Calvo Álvarez, V. Arora
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引用次数: 0

Abstract

Though traditional deterministic finite element model updating (FEMU) methods are being used in the industry with good results, however, these methods do not include the small variations in uncertainties. These uncertainties arise due to unknown experimental errors or variability in nominally identical dynamic systems. It is necessary to resort to the stochastic finite element model updating (SFEMU) methods to deal with these problems. Although any modal parameter can be used to update a dynamic system, this paper explores the possibility of updating a dynamic system using only natural frequencies, because natural frequencies can be obtained more accurately with lesser computational cost than obtaining any other modal parameters. In this paper, stochastic finite element model updating procedure has been proposed which uses natural frequencies with equal weighting factors to perform the stochastic finite element model updating. The effectiveness of the proposed stochastic finite element model updating procedure is demonstrated by two numerical examples and one experimental example. The results show that the stochastic finite element model updating procedure using only natural frequencies with equal weighting factors can be used for accurate quantification of uncertainties in the dynamic systems.
基于自然频率的等权因子随机有限元模型更新
虽然传统的确定性有限元模型更新(FEMU)方法在行业中得到了应用,并取得了良好的效果,但这些方法并没有考虑到不确定性的微小变化。这些不确定性是由于在名义上相同的动态系统中未知的实验误差或可变性引起的。需要借助于随机有限元模型更新(SFEMU)方法来处理这些问题。尽管可以使用任何模态参数来更新动态系统,但本文探索了仅使用固有频率更新动态系统的可能性,因为与获取任何其他模态参数相比,固有频率可以更准确地获得,且计算成本更低。本文提出了一种随机有限元模型更新程序,该程序采用等权重因子的固有频率对随机有限元模型进行更新。通过两个数值算例和一个实验算例验证了所提出的随机有限元模型修正方法的有效性。结果表明,仅使用等权重因子的固有频率的随机有限元模型更新程序可用于准确量化动态系统中的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Structural Engineering
International Journal of Structural Engineering Engineering-Civil and Structural Engineering
CiteScore
2.40
自引率
23.10%
发文量
24
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