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首页> 外文期刊>International journal of mechanics and materials in design >An efficient inverse algorithm for load identification of stochastic structures
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An efficient inverse algorithm for load identification of stochastic structures

机译：一种有效的随机结构载荷识别算法

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摘要

Force identification of stochastic structures is very important in science and engineering, which also leads to the challenges in the field of computational mechanics. Monte-Carlo simulation (MCS) method is a robust and effective random simulation technique for the dynamic load identification problem of the stochastic structure. However, the MCS method needs large computational cost and is also inefficient for practical engineering applications because of the requirement of a large quantity of samples. In this paper, in order to improve computational efficiency of MCS, a novel algorithm is proposed based on the modified conjugate gradient method and matrix perturbation method. First, the new developed algorithm exploits matrix perturbation method to transform dynamic load identification problems for stochastic structures into equivalent deterministic dynamic load identification problems. Then the dynamic load identification can be realized using modified conjugate gradient method. Finally, the statistical characteristics of identified force are analyzed. The accuracy and efficiency of the newly developed computational method are demonstrated by several numerical examples. It has been found that the newly proposed algorithm can significantly improve the computational efficiency of MCS and it is believed to be a powerful tool for solving the dynamic load identification for stochastic structures.
机译：随机结构的力识别在科学和工程中非常重要，这也导致了计算力学领域的挑战。 Monte-Carlo仿真（MCS）方法是一种稳健而有效的随机仿真技术，用于随机结构的动态载荷识别问题。然而，MCS方法需要大的计算成本，并且由于需要大量样品，因此实际工程应用也是低效的。本文基于改进的共轭梯度法和矩阵扰动方法，提出了一种新的算法，提出了一种新的算法。首先，新的发达算法利用矩阵扰动方法将随机结构的动态负载识别问题转换为等效的确定性动态负载识别问题。然后可以使用修改的共轭梯度方法实现动态负载识别。最后，分析了鉴定力的统计特征。几个数值例子证明了新开发的计算方法的准确性和效率。已经发现，新的算法可以显着提高MCS的计算效率，并且据信是一种强大的工具，用于解决随机结构的动态载荷识别。

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