Demagnetizing field in single crystal ferromagnetic shape memory alloys

Eberle, J Lance; Feigenbaum, Heidi P.; Ciocanel, Constantin

doi:10.1088/1361-665x/aaf20e

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…First, the Guiel et al experiments were strain-controlled while the model is stress controlled. Next, the field was measured at the surface in these tests, which accordingly to Eberle et al [22] should not be used as the input applied field. Finally, the Guiel et al experiments, which were performed at 15 Hz frequency showed evidence that inertial effects were evident (i.e.…”

Section: Methodsmentioning

confidence: 99%

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Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

Emu

2023

Smart Mater. Struct.

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Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. Under variable stress and a constant bias field, MSMAs can be used in power harvesting or sensing devices, e.g., in structural health monitoring applications. This behavior is primarily a result of the approximately tetragonal unit cell whose magnetic easy axis is approximately aligned with the short axis of the unit cell within the Ni-Mn-Ga single crystals. Under an applied field, the magnetic easy axis tends to align with the external field. Similarly, under an applied compressive force, the short side of the unit cell tends to align with the direction of the force. This work introduced a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D’silva et al. [1]. This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and field controlled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al. [2]. Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was trained using the experimental data (stress, strain, magnetic field, & emf) so that it can make a reasonably better prediction.

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Section: Methodsmentioning

confidence: 99%

“…where H is the internal magnetic field experienced by the MSMA specimen, H app is the external magnetic field applied on the specimen, D is the demagnetization tensor [22].…”

Section: Model Derivationmentioning

confidence: 99%

Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

Emu

2023

Smart Mater. Struct.

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Magnetic Characterization of Ferromagnetic Shape Memory Components Under Defined Mechanical Loading

Ehle

Neumeister

Haufe

et al. 2020

Shap. Mem. Superelasticity

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Comparison of Model and Experimental Characteristics of Field Between Spheric Pole Pieces at Faraday Balance

Sandulyak¹,

Sandulyak²,

Tkachenko³

et al. 2019

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Experimental coordinate characteristics of magnetic field induction and its gradient are obtained between pole pieces of Faraday balance electromagnetic system. Pole pieces have spheric form that is preferable by possibility of obtaining area with a stable gradient as a working area for specimen’s positioning. Alternative characteristics which are obtained by modeling at the program COMSOL Multiphysics are shown using the same values of supply current and distance between pole pieces as at the experiment. It is shown that together with qualitative commonality of model and experimental characteristics difference of its data at the working area are up to 11 % (by induction), 24 % (by gradient), hereby testify about probable error of conclusive result which is up to 35 %, and up to 5 % by coordinate of gradient extremum. Questions about determining correction factor are discussed (normalizing factor for model characteristic); at its proved value equals 0,86 (e.g. at starting exclude of 14 % «systematic» error, probably obtained at the case of using results after modeling) a differences of mentioned data are up to 4, 5, 5% accordingly.

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Demagnetizing field in single crystal ferromagnetic shape memory alloys

Cited by 3 publications

References 22 publications

Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

Magnetic Characterization of Ferromagnetic Shape Memory Components Under Defined Mechanical Loading

Comparison of Model and Experimental Characteristics of Field Between Spheric Pole Pieces at Faraday Balance

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