scholarly journals Harmonic analysis and modelling of magnetisation process in soft ferromagnetic material

2017 ◽  
Vol 30 (1) ◽  
pp. 121-136
Author(s):  
Branko Koprivica ◽  
Ioan Dumitru ◽  
Alenka Milovanovic ◽  
Ovidiu Caltun
Keyword(s):  
Magnetic Field ◽  
Hysteresis Loop ◽  
Magnetic Induction ◽  
Domain Walls ◽  
Magnetic Hysteresis ◽  
Magnetic Domain ◽  
Fast Fourier Transformation ◽  
Induced Voltage ◽  
Harmonic Content ◽  
Excitation Magnetic Field

The aim of this paper is to present a research of magnetic hysteresis loops of a toroidal ferromagnetic core made of electrical steel. The experimental results of induced voltage, magnetic induction and hysteresis loop obtained at different frequencies of the sinusoidal excitation magnetic field have been presented. The harmonic content of the induced voltage and magnetic induction have been calculated using Fast Fourier Transformation. Observed variation of higher harmonics with frequency has been correlated to the mechanism of magnetic domain walls damping. A variation of harmonics of the magnetic induction with the amplitude of the excitation magnetic field has been analysed and a proper mathematical model has been proposed. Furthermore, the influence of the triangularly shaped excitation magnetic field and the distorted shape excitation that produces sinusoidal induction on the shape of hysteresis loop and harmonic content of the induced voltage and the magnetic induction has been analysed and discussed.

MAGNETIC HYSTERESIS OF HIGH-TEMPERATURE YBa2Cu3Ox-AgO SUPERCONDUCTORS: EXPLANATION OF MAGNETIC SUSPENSION

1988 ◽  
Vol 02 (07) ◽  
pp. 869-874 ◽  
Author(s):  
C.Y. HUANG ◽  
Y. SHAPIRA ◽  
E.J. MCNIFF ◽  
P.N. PETERS ◽  
B.B. SCHWARTZ ◽  
...  
Keyword(s):  
Magnetic Field ◽  
High Temperature ◽  
Simple Model ◽  
Hysteresis Loop ◽  
Applied Magnetic Field ◽  
Magnetic Hysteresis ◽  
Magnetic Suspension ◽  
Pinning Centers ◽  
Strong Pinning

We have measured the magnetization M of superconducting YBa 2 Cu 3 O x- AgO composites with T c approximately equal to 92K as a function of an applied magnetic field H at 77 and 87K. A very pronounced M-H hysteresis loop occurs even at 87K, indicating the presence of extremely strong pinning centers. The results of these measurements, together with a simple model, explain quantitatively why these superconductors could be suspended below a magnet.

Effects of Magnetic Domain Walls on the Anisotropic Magnetoresistance in NiFe Nanowires

2015 ◽  
Vol 15 (10) ◽  
pp. 7620-7623 ◽  
Author(s):  
Chunghee Nam
Keyword(s):  
Magnetic Field ◽  
Magnetic Force ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Anisotropic Magnetoresistance ◽  
Magnetic Force Microscope ◽  
Micromagnetic Simulations ◽  
Magnetic Domain Walls ◽  
Magnetic Wires ◽  
Low Magnetic Field

We show that a type of magnetic domain walls (DWs) can be monitored by anisotropic magnetoresistance (AMR) measurements due to a specific DW volume depending on the DW type in NiFe magnetic wires. A circular DW injection pad is used to generate DWs at a low magnetic field, resulting in reliable DW introduction into magnetic wires. DW pinning is induced by a change of DW energy at an asymmetric single notch. The injection of DW from the circular pad and its pinning at the notch is observed by using AMR and magnetic force microscope (MFM) measurements. A four-point probe AMR measurement allows us to distinguish the DW type in the switching process because DWs are pinned at the single notch, where voltage probes are closely placed around the notch. Two types of AMR behavior are observed in the AMR measurements, which is owing to a change of DW structures. MFM images and micromagnetic simulations are consistent with the AMR results.

scholarly journals Neuromorphic Computation With a Single Magnetic Domain Wall

2021 ◽  
Author(s):  
Razvan V. Ababei ◽  
Matthew O. A. Ellis ◽  
Ian T. Vidamour ◽  
Dhilan S. Devadasan ◽  
Dan A. Allwood ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Domain Wall ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Reservoir Computing ◽  
Model Complex

Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1d collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

Metal–oxide films with magnetically-modulated nanoporous architectures

2001 ◽  
Vol 16 (6) ◽  
pp. 1686-1693 ◽  
Author(s):  
Craig A. Grimes ◽  
R. Suresh Singh ◽  
Elizabeth C. Dickey ◽  
Oomman K. Varghese
Keyword(s):  
Magnetic Field ◽  
Metal Oxide ◽  
Coming Out ◽  
Domain Walls ◽  
Substrate Surface ◽  
Sol Gel ◽  
Oxide Films ◽  
Magnetic Domain ◽  
Domain Size ◽  
Metal Oxide Films

A magnetically-driven method for controlling nanodimensional porosity in sol-gel-derived metal–oxide films, including TiO2, Al2O3, and SnO2, coated onto ferromagnetic amorphous substrates, such as the magnetically-soft Metglas1 alloys, is described. On the basis of the porous structures observed dependence on external magnetic field, a model is suggested to explain the phenomena. Under well-defined conditions it appears that the sol particles coming out of solution, and undergoing Brownian motion, follow the magnetic field lines oriented perpendicularly to the substrate surface associated with the magnetic domain walls of the substrate; hence the porosity developed during solvent evaporation correlates with the magnetic domain size.

White Synchrotron Radiation Topography of (11̄0) Nickel Single Crystal

1982 ◽  
Vol 37 (5) ◽  
pp. 505-511
Author(s):  
J. D. Stephenson
Keyword(s):  
Magnetic Field ◽  
Synchrotron Radiation ◽  
Single Crystal ◽  
Domain Structure ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Nickel Single Crystal ◽  
Magnetic Domain Walls ◽  
Equivalent Field ◽  
Nickel Crystal

Changes in 70.53° magnetic domain structure on the surface of a perfect (11̄0) nickel crystal have been observed using white synchrotron X-radiation topography. The crystal was influenced by a variable [11̄0] magnetic field. At field strengths ≿ 100 A/m [111̄]-spike domains, thought to be traces of [011], 70.53° (oblique) magnetic domain walls, appeared within [111]-bands (0.4 mm wide) in the topographs. Reversal of the field produced similar spikes at equivalent field values but in different regions of the crystal.

Experimental Study and Numerical Modelling of Magnetic Domain Walls Drift in Ferrite Garnet Crystals

2014 ◽  
Vol 215 ◽  
pp. 437-442 ◽  
Author(s):  
Lidia A. Pamyatnykh ◽  
Georgy A. Shmatov ◽  
Mikhail S. Lysov ◽  
Sergey E. Pamyatnykh ◽  
Dmitry S. Mehonoshin
Keyword(s):  
Magnetic Field ◽  
Experimental Study ◽  
Numerical Modelling ◽  
Linear Function ◽  
Qualitative Agreement ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Ferrite Garnet ◽  
Magnetic Domain Walls ◽  
Oscillating Magnetic Field

The results of study of domain walls oscillations in harmonic magnetic field H = H0sin (2πft) oriented perpendicular to ferrite garnet (TbErGd)3(FeAl)5O12 (111) sample plate for amplitudes that include the drift of domain walls are reported. Numerical modelling of domain walls motion was performed for frequencies f~102 Hz, where the drift is observed experimentally. Comparison of results of numerical modelling with experimental results shows their qualitative agreement. It was established that domain walls oscillations amplitude is a linear function of amplitude of oscillating magnetic field.

scholarly journals Neuromorphic computation with a single magnetic domain wall

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Razvan V. Ababei ◽  
Matthew O. A. Ellis ◽  
Ian T. Vidamour ◽  
Dhilan S. Devadasan ◽  
Dan A. Allwood ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Domain Wall ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Reservoir Computing ◽  
Model Complex

AbstractMachine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

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