Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest
Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest
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DOI：
10.1007/s00366-023-01868-x
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发表时间：
2023-07
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通讯作者：
J. Elgy;P. Ledger
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文献类型：
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作者：
J. Elgy;P. Ledger
极化率张量（MPT）是一种经济的导电磁性物体的特征，它可以帮助识别隐藏的目标在金属探测。MPT的系数取决于多个感兴趣的参数，包括物体形状、大小、电导率、磁导率和激励频率。的系数的计算遵循从后处理的涡流传输问题的数值求解，使用高阶有限元。为了减少计算成本的多个不同的参数构建这些特征，我们比较了三种方法，MPT可以有效地计算二维参数集，具有不同程度的代码入侵。我们比较，数值例子，神经网络回归MPT特征值与基于投影的降阶模型（ROM）和神经网络增强ROM（POD-NN）预测MPT系数。
The magnetic polarizability tensor (MPT) is an economical characterisation of a conducting magnetic object, which can assist with identifying hidden targets in metal detection. The MPT’s coefficients depend on multiple parameters of interest including the object shape, size, electrical conductivity, magnetic permeability, and the frequency of excitation. The computation of the coefficients follow from post-processing an eddy current transmission problem solved numerically using high-order finite elements. To reduce the computational cost of constructing these characterisations for multiple different parameters, we compare three methods by which the MPT can be efficiently calculated for two-dimensional parameter sets, with different levels of code invasiveness. We compare, with numerical examples, a neural network regression of MPT eigenvalues with a projection-based reduced order model (ROM) and a neural network enhanced ROM (POD–NN) for predicting MPT coefficients.