Efficient computation of magnetic polarizability tensor spectral signatures for object characterisation in metal detection

Elgy, James; Ledger, Paul David

Abstract

Purpose
Magnetic polarizability tensors (MPTs) provide an economical characterisation of conducting magnetic metallic objects and their spectral signature can aid in the solution of metal detection inverse problems, such as scrap metal sorting, searching for unexploded ordnance in areas of former conflict and security screening at event venues and transport hubs. In this work, the authors aim to discuss methods for efficiently building large dictionaries for classification approaches.

Design/methodology/approach
Previous work has established explicit formulae for MPT coefficients, underpinned by a rigorous mathematical theory. To assist with the efficient computation of MPTs at differing parameters and objects of interest, this work applies new observations about the way the MPT coefficients can be computed. Furthermore, the authors discuss discretisation strategies for hp-finite elements on meshes of unstructured tetrahedra combined with prismatic boundary layer elements for resolving thin skin depths and using an adaptive proper orthogonal decomposition (POD) reduced-order modelling methodology to accelerate computations for varying parameters.

Findings
The success of the proposed methodologies is demonstrated using a series of examples. A significant reduction in computational effort is observed across all examples. The authors identify and recommend a simple discretisation strategy and improved accuracy is obtained using adaptive POD.

Originality/value
The authors present novel computations, timings and error certificates of MPT characterisations of realistic objects made of magnetic materials. A novel postprocessing implementation is introduced and an adaptive POD algorithm is demonstrated.

Citation

Elgy, J., & Ledger, P. D. (in press). Efficient computation of magnetic polarizability tensor spectral signatures for object characterisation in metal detection. Engineering Computations, https://doi.org/10.1108/ec-04-2024-0343

• Journal Article Type: Article
• Acceptance Date: Oct 24, 2024
• Online Publication Date: Oct 24, 2024
• Deposit Date: Nov 12, 2024
• Publicly Available Date: Jan 10, 2025
• Journal: Engineering Computations
• Print ISSN: 0264-4401
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1108/ec-04-2024-0343
• Public URL: https://keele-repository.worktribe.com/output/975459