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Multilevel adaptive cross approximation for efficient modeling of 3D arbitrary shaped eddy current NDE problems

机译:多级自适应交叉逼近可有效建模3D任意形状涡流NDE问题

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摘要

In this article, the multilevel adaptive cross approximation (MLACA) algorithm is presented to accelerate the boundary element method (BEM) for eddy current nondestructive evaluation (NDE) 3D problems involving arbitrary shapes. The Stratton-Chu formula, which does not have the low frequency breakdown issue, has been selected for modeling. The equivalent electric and magnetic surface currents are expanded with Rao-Wilton-Glisson (RWG) vector basis functions while the normal component of the magnetic field is expanded with pulse basis functions. The MLACA compresses the rank deficient matrices with the ACA and the butterfly algorithm. We improve the efficiency of MLACA by truncating the integral kernels after a certain distance and applying the multi-stage (level) algorithm adaptively based on the criteria for different operators to further decrease the memory and CPU time requirements while keeping almost the same accuracy comparing with the traditional MLACA. The proposed method is especially helpful to deal with the large solution domain issue of the BEM for eddy current problems. Numerical predictions are compared with the analytical, the semi-analytical predictions and the experimental results for 3D eddy current NDE problems of practical interest to demonstrate the robustness and efficiency of the proposed method.
机译:在本文中,提出了多级自适应交叉逼近(MLACA)算法,以加速涉及任意形状的涡流无损评估(NDE)3D问题的边界元方法(BEM)。选择了不具有低频击穿问题的Stratton-Chu公式进行建模。用Rao-Wilton-Glisson(RWG)矢量基函数扩展等效的电磁表面电流,而使用脉冲基函数扩展磁场的法向分量。 MLACA使用ACA和Butterfly算法压缩秩不足矩阵。我们通过在一定距离后截断整数内核并根据不同运算符的标准自适应地应用多级(级别)算法来提高MLACA的效率,以进一步减少内存和CPU时间要求,同时保持与传统的MLACA。所提出的方法对于解决涡流问题的边界元法的大求解域问题特别有帮助。将数值预测与实际感兴趣的3D涡流NDE问题的分析,半分析预测和实验结果进行了比较,以证明所提出方法的鲁棒性和有效性。

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