J
J.D. Lavers
Researcher at University of Toronto
Publications - 106
Citations - 1378
J.D. Lavers is an academic researcher from University of Toronto. The author has contributed to research in topics: Eddy current & Finite element method. The author has an hindex of 19, co-authored 106 publications receiving 1338 citations.
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A simple method of estimating the minor loop hysteresis loss in thin laminations
TL;DR: In this article, a simple and practical method of correcting the hysteresis loss in a thin lamination for the effects of minor loops is described, and the necessary correction is applied to the losses that would occur under conditions of sinusoidal flux density.
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A method for circuit connections in time-dependent eddy current problems
TL;DR: In this article, the authors considered eddy current diffusion problems in which the electromagnetic field is computed in 2D but external circuit connections, between the conductors with eddy currents are taken into account.
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Prediction of core losses for high flux densities and distorted flux waveforms
J.D. Lavers,Paul P. Biringer +1 more
TL;DR: In this paper, the authors considered the problem of estimating the core losses in a thin magnetic steel lamination when the driving flux contains not only the fundamental, but also odd harmonic components.
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Numerical solution methods for electroheat problems
TL;DR: In this article, the use of numerical methods in simulating and solving problems that arise in the electroheat industry is reviewed. But the focus is on the coupled electrothermal and induction stirring problems that are typical of this industry.
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Sequential Optimization Method for the Design of Electromagnetic Device
TL;DR: Three sequential optimization methods, sequential least square method, sequential Kriging method, and sequential linear Bayesian method, are presented for the optimization design of electromagnetic device and practical application illustrates that the number of finite element sample points is less than 1/10 compared with that by direct optimization method, while the optimal results are even better than that bydirect optimization method.