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

机译:用于3D任意形状涡流NDE问题的高效建模的多级自适应交叉近似

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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近似(MLACA)算法以加速涉及任意形状的涡流非破坏性评估(NDE)3D问题的边界元方法(BEM)。已经选择了没有低频分解问题的Stratton-Chu公式来建模。等效电和磁性表面电流与Rao-Wilton-Glisson(RWG)矢量基函数扩展,而磁场的正常分量以脉冲基函数扩展。 MLACA用ACA和蝶形算法压缩秩缺陷矩阵。通过在一定距离之后截断积分内核并自适应地基于不同运营商的标准来改善积分内核来提高MLACA的效率,以进一步降低存储器和CPU时间要求,同时保持与...保持几乎相同的准确性传统的Mlaca。该方法尤为有助于处理BEM的大型解决方案域问题,用于涡流问题。将数值预测与分析,半分析预测和实际兴趣稳健性和效率进行了分析,半分析预测和实验结果,以证明所提出的方法的鲁棒性和效率。

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