Characterization of Magnetic Steels for the FCC-ee Magnet Prototypes

Anglada, Jaime Renedo; Arpaïa, Pasquale; Buzio, Marco; Pentella, Mariano; Petrone, Carlo

doi:10.1109/i2mtc43012.2020.9129153

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…The chemical composition of S355 is as follows: Mn-1.45, Al-0.33, P-0.23, Si-0.21, C-0.17, S-0.08 [32]. The exemplary magnetic properties of the steel S355 are as follows [35]: a relative peak permeability of 1500, a saturation point of 1.7 T at 6.9 kA/m, a coercive field of 310 A/m, and a residual flux density of 1 T (measured on the major loop).…”

Section: Methodsmentioning

confidence: 99%

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

et al. 2022

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This paper proposes and experimentally investigates a novel nondestructive testing method for ferromagnetic elements monitoring, the Magnetic Recording Method (MRM). In this method, the inspected element must be magnetized in a strictly defined manner before operation. This can be achieved using an array of permanent magnets arranged to produce a quasi-sinusoidal magnetization path. The magnetic field caused by the original residual magnetization of the element is measured and stored for future reference. After the operation or loading, the magnetic field measurement is repeated. Analysis of relative changes in the magnetic field (for selected components) allows identifying applied stress. The proposed research methodology aims to provide information on the steel structure condition unambiguously and accurately. An interpretation of the results without referring to the original magnetization is also possible but could be less accurate. The method can be used as a standard technique for NDT (Non-Destructive Testing) or in structural health monitoring (SHM) systems.

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Section: Methodsmentioning

confidence: 99%

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

et al. 2022

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“…Then, a diagonal tensor can be obtained. By that, the field components of

\vec{H}

and

\vec{B}

are decoupled and measurement data obtained from experiments, 29,30 can be directly incorporated in the data‐driven solver. For that reason, we consider only diagonal weighting tensors for the rest of this work.…”

Section: Data‐driven Magnetostatic Solversmentioning

confidence: 99%

Data‐driven solvers for strongly nonlinear material response

Galetzka

Loukrezis

Gersem

2021

Numerical Meth Engineering

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This work presents a data-driven magnetostatic finite-element solver that is specifically well suited to cope with strongly nonlinear material responses. The data-driven computing framework is essentially a multiobjective optimization procedure matching the material operation points as closely as possible to given material data while obeying Maxwell's equations. Here, the framework is extended with heterogeneous (local) weighting factors-one per finite element-equilibrating the goal function locally according to the material behavior. This modification allows the data-driven solver to cope with unbalanced measurement data sets, that is, data sets suffering from unbalanced space filling. This occurs particularly in the case of strongly nonlinear materials, which constitute problematic cases that hinder the efficiency and accuracy of standard data-driven solvers with a homogeneous (global) weighting factor. The local weighting factors are embedded in the distance-minimizing data-driven algorithm used for noiseless data, likewise for the maximum entropy data-driven algorithm used for noisy data. Numerical experiments based on a quadrupole magnet model with a soft magnetic material show that the proposed modification results in major improvements in terms of solution accuracy and solver efficiency. For the case of noiseless data, local weighting factors improve the convergence of the data-driven solver by orders of magnitude. When noisy data are considered, the convergence rate of the data-driven solver is doubled.

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“…In particular, we consider a low-cost structural steel, called S355. The used measurement data are taken from Anglada et al (2020). In addition to the data-driven solution, a solution obtained with a conventional solver is computed as well.…”

Section: Real-world Measurement Datamentioning

confidence: 99%

Three-dimensional data-driven magnetostatic field computation using real-world measurement data

Galetzka

Loukrezis

Gersem

2021

COMPEL

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Purpose The purpose of this paper is to present the applicability of data-driven solvers to computationally demanding three-dimensional problems and their practical usability when using real-world measurement data. Design/methodology/approach Instead of using a hard-coded phenomenological material model within the solver, the data-driven computing approach reformulates the boundary value problem such that the field solution is directly computed on raw measurement data. The data-driven formulation results in a double minimization problem based on Lagrange multipliers, where the sought solution must conform to Maxwell’s equations while at the same time being as close as possible to the available measurement data. The data-driven solver is applied to a three-dimensional model of a direct current electromagnet. Findings Numerical results for data sets of increasing cardinality verify that the data-driven solver recovers the conventional solution. Additionally, the practical usability of the solver is shown by using real-world measurement data. This work concludes that the data-driven magnetostatic finite element solver is applicable to computationally demanding three-dimensional problems, as well as in cases where a prescribed material model is not available. Originality/value Although the mathematical derivation of the data-driven problem is well presented in the referenced papers, the application to computationally demanding real-world problems, including real measurement data and its rigorous discussion, is missing. The presented work closes this gap and shows the applicability of data-driven solvers to challenging, real-world test cases.

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Characterization of Magnetic Steels for the FCC-ee Magnet Prototypes

Cited by 9 publications

References 17 publications

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

Data‐driven solvers for strongly nonlinear material response

Three-dimensional data-driven magnetostatic field computation using real-world measurement data

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