Magnetic Flux Controllers in Induction Heating and Melting
MAGNETIC FLUX CONTROLLERS are materials other than the copper coil that are used in induction systems to alter the flow of the magnetic field. Magnetic flux controllers used in power supplying components are not considered in this article. Magnetic flux controllers have been in existence since the development of the induction technique. Michael Faraday used two coils of wire wrapped around an iron core in his experiments that led to Faraday’s law of electromagnetic induction, which states that the electromotive force (emf) induced in a circuit is directly proportional to the time rate of change of the magnetic flux through the circuit. After the development of the induction principle, magnetic flux controllers, in the formof stacks of laminated steel, found widespread use in the development of transformers for more efficient transmission of energy (Ref 1, 2). Magnetic cores gained widespread use in the transformer industry because they increased the amount of magnetic flux produced with the same alternating current. The higher the magnetic flux, the higher the emf, which results in an increase in energy transfer efficiency from the primary winding to the secondary winding. Similar to transformers, magnetic cores were used on early furnaces for induction melting (Ref 1, 2). The benefits of magnetic flux controllers vary depending on the application. For induction heating, magnetic flux controllers can provide favorable and unfavorable paths for magnetic flux to flow, resulting in increased heating in desired areas and reduced the heating in undesirable areas, respectively. Magnetic flux controllers are not used in every induction heating application, but their use has increased (Ref 3, 4).
Solving 1D non‐linear magneto quasi‐static Maxwell's equations using neural networks
Funding information Gottfried Wilhelm Leibniz Universität Hannover Abstract Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, called Maxwell’s equations. “Quasi-static” approximations emerge from neglecting particular couplings of electric and magnetic field related quantities. In case of slowly time varying fields, if inductive and resistive effects have to be considered, whereas capacitive effects can be neglected, the magneto quasi-static (MQS) approximation applies. The solution of the MQS Maxwell’s equations, traditionally obtained with finite differences and elements methods, is crucial in modelling EM devices. In this paper, the applicability of an unsupervised deep learning model is studied in order to solve MQS Maxwell’s equations, in both frequency and time domain. In this framework, a straightforward way to model hysteretic and anhysteretic non-linearity is shown. The introduced technique is used for the field analysis in the place of the classical finite elements in two applications: on the one hand, the B–H curve inverse determination of AISI 4140, on the other, the simulation of an induction heating process. Finally, since many of the commercial FEM packages do not allow modelling hysteresis, it is shown how the present approach could be further adopted for the inverse magnetic properties identification of new magnetic flux concentrators for induction applications.
Industrial inorganic pigments
General Chemical and Physical Properties Behavior of Pigments in Binders White Pigments Titanium Dioxide Zinc Sulfide Colored Pigments Oxides and Hydroxides Cadmium Pigments Bismuth Pigments Ultramarine Pigments Iron Blue Pigments Carbon Black Properties Production Processes Specialty Pigments Magnetic Pigments Anticorrosive Pigments Luster Pigments Transparent Pigments.