Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest

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,...

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Bibliographic Details
Published in:Engineering with computers 2023-12, Vol.39 (6), p.4061-4076
Main Authors: Elgy, James, Ledger, Paul D.
Format: Article
Language:English
Subjects:
Approximation
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Coefficients
Computer Science
Computer-Aided Engineering (CAD
Computers
Control
Eddy currents
Eigenvalues
Electrical resistivity
Magnetic permeability
Math. Applications in Chemistry
Mathematical and Computational Engineering
Mathematical models
Neural networks
Original Article
Parameters
Permeability
Reduced order models
Software
Systems Theory
Target detection
Tensors
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Summary: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.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-023-01868-x