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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >An adaptive multiscale approach for identifying multiple flaws based on XFEM and a discrete artificial fish swarm algorithm
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An adaptive multiscale approach for identifying multiple flaws based on XFEM and a discrete artificial fish swarm algorithm

机译：基于XFEM和离散人工鱼群算法的自适应多尺度缺陷识别方法

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An adaptive multiscale approach for identifying multiple flaws in structures is proposed in this paper. The approach includes a two-step process in which a coarse-scale search initially identifies the candidate subdomains and then a fine-scale search of the narrow subdomains captures the true flaws. The extended finite element method (XFEM) is employed to solve the forward problem because it does not require re-meshing in each iteration. A discrete artificial fish swarm (DAFS) algorithm with adaptive vision is adopted to accelerate the convergence of the objective function. The probable subdomains are output via K-means clustering with an initial estimated number of flaws Then, the artificial bee colony (ABC) algorithm is employed to capture the true flaws and remove the false flaws. The DAFS algorithm is a single-population algorithm, while the ABC algorithm is a multi-population algorithm. Three numerical examples are studied to evaluate the accuracy and performance of the proposed approach. Circular and elliptical flaws and the effects of measurement noise are considered to test the robustness. The results show that this approach can effectively quantify and identify multiple internal flaws without any prior knowledge of their quantity. Furthermore, the strategy is more efficient than previously proposed approaches. (C) 2018 Elsevier B.V. All rights reserved.
机译：本文提出了一种用于识别结构中多个缺陷的自适应多尺度方法。该方法包括两步过程，其中粗略搜索最初会识别出候选子域，然后对狭窄子域进行精细搜索会捕获真正的缺陷。扩展有限元方法（XFEM）用于解决前向问题，因为它不需要在每次迭代中重新划分网格。采用具有自适应视觉的离散人工鱼群算法来加速目标函数的收敛。通过K-means聚类输出具有初始估计缺陷数的可能子域，然后，使用人工蜂群（ABC）算法捕获真实缺陷并消除错误缺陷。 DAFS算法是单人口算法，而ABC算法是多人口算法。研究了三个数值示例，以评估该方法的准确性和性能。考虑圆形和椭圆形缺陷以及测量噪声的影响，以测试鲁棒性。结果表明，该方法可以有效地量化和识别多个内部缺陷，而无需事先知道它们的数量。此外，该策略比以前提出的方法更有效。 （C）2018 Elsevier B.V.保留所有权利。

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