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Finite element model updating of a large structure using multi-setup stochastic subspace identification method and bees optimization algorithm

机译:大结构的有限元模型使用多设置随机子空间识别方法和蜜蜂优化算法

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In the present contribution, operational modal analysis in conjunction with bees optimization algorithm are utilized to update the finite element model of a solar power plant structure. The physical parameters which required to be updated are uncertain parameters including geometry, material properties and boundary conditions of the aforementioned structure. To determine these uncertain parameters, local and global sensitivity analyses are performed to increase the solution accuracy. An objective function is determined using the sum of the squared errors between the natural frequencies calculated by finite element method and operational modal analysis, which is optimized using bees optimization algorithm. The natural frequencies of the solar power plant structure are estimated by multi-setup stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. The proposed algorithm is efficiently implemented on the solar power plant structure located in Shahid Chamran university of Ahvaz, Iran, to update parameters of its finite element model. Moreover, computed natural frequencies by numerical method are compared with those of the operational modal analysis. The results indicate that, bees optimization algorithm leads accurate results with fast convergence.
机译:在本贡献中,利用与Bees优化算法结合的操作模态分析来更新太阳能发电厂结构的有限元模型。需要更新的物理参数是不确定参数,包括上述结构的几何形状,材料特性和边界条件。为了确定这些不确定的参数,进行局部和全局敏感性分析以提高解决方案精度。利用由有限元方法和操作模态分析计算的自然频率之间的平方误差的总和确定目标函数,其使用Bees优化算法进行了优化。太阳能发电厂结构的自然频率估计了多于内置随机子空间识别方法,被认为是在操作模态分析中的强大有效的方法。该算法在位于伊朗的Shahid Chamran大学的太阳能发电厂结构上有效地实施了其有限元模型的参数。此外,通过数值方法计算的自然频率与操作模态分析的计算。结果表明,蜜蜂优化算法通过快速收敛引发准确的结果。

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