A Superconducting Permeameter for Characterizing Soft Magnetic Materials at High Fields

Arpaïa, Pasquale; Buzio, Marco; Bermúdez, Susana Izquierdo; Liccardo, Annalisa; Parrella, Alessandro; Pentella, Mariano; Ramos, Pedro M.; Stubberud, Edvard

doi:10.1109/tim.2019.2945552

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…The offset is then assumed to be constant for the entire duration of the measurement, which corresponds to assuming a linear drift. FFA2—Online updating algorithm: This algorithm is proposed in [ 33 ], and it was adapted to run online. The offset voltage is modeled as a piece-wise constant function, where the estimate is updated periodically by averaging over intervals of duration

s once the magnetic field reaches a stable plateau (see Figure 1 ), after the eddy current transient is fully damped.…”

Section: Experimental Resultsmentioning

confidence: 99%

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

Arpaïa

Buzio

Capua

et al. 2021

Sensors

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Sensing coils are inductive sensors commonly used to measure magnetic fields, such as those generated by electromagnets used in many kinds of industrial and scientific applications. Inductive sensors rely on integrating the output voltage at the coil’s terminals in order to obtain flux linkage, which may suffer from the magnification of low-frequency noise resulting in a drifting integrated signal. This article presents a method for the cancellation of integrator drift. The method is based on a first-order linear Kalman filter combining the data from the coil and a second sensor. Two case studies are presented. In the first one, the second sensor is a Hall probe, which senses the magnetic field directly. In a second case study, the magnet’s excitation current was used instead to provide a first-order approximation of the field. Experimental tests show that both approaches can reduce the measured field drift by three orders of magnitude. The Hall probe option guarantees, in addition, one order of magnitude better absolute accuracy than by using the excitation current.

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s once the magnetic field reaches a stable plateau (see Figure 1 ), after the eddy current transient is fully damped.…”

Section: Experimental Resultsmentioning

confidence: 99%

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

Arpaïa

Buzio

Capua

et al. 2021

Sensors

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“…This is especially relevant for strongly nonlinear material responses, where the nonlinearity can lead to certain regions of the material curve being densely sampled, thus the corresponding data set can be characterized as "well behaved," while other regions are represented only by sparse and irregular data points. 29 In the following we analyze the behavior of the data-driven solver exactly for such cases. Let us assume that a state × ∈  has been identified as the closest to conform with Maxwell's equations.…”

Section: Local Weighting Factorsmentioning

confidence: 99%

“…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%

“…Particularly in cases where the states in the measurement set

𝒟

depart from the Maxwell‐conforming set

ℳ

, the global weighting factor introduced in Section 3.1 faces major difficulties to produce an accurate solution. This is especially relevant for strongly nonlinear material responses, where the nonlinearity can lead to certain regions of the material curve being densely sampled, thus the corresponding data set can be characterized as “well behaved,” while other regions are represented only by sparse and irregular data points 29 . In the following we analyze the behavior of the data‐driven solver exactly for such cases.…”

Section: Data‐driven Magnetostatic Simulation With Noiseless Datamentioning

confidence: 99%

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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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“…The motivation behind this work lies in the prediction of the dynamic behaviour of materials used in the manufacturing of magnets for particle accelerators. As an example, ARMCO is used as a test material being used as part of the superconducting magnets for the High Luminosity upgrade of the Large Hadron Collider at the European Nuclear Research Centre (CERN) [28,29], where pulsed fields are employed. Hence, the knowledge of the material's dynamic behaviour, which is not easily modelled, is required and this work attempts to propose an accurate model, trained with a limited amount of measurement data, which can also be used at a moderate computational cost.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

et al. 2020

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In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model’s parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model’s potential in the field of materials science.

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A Superconducting Permeameter for Characterizing Soft Magnetic Materials at High Fields

Cited by 6 publications

References 25 publications

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

Data‐driven solvers for strongly nonlinear material response

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

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