2020
DOI: 10.3390/app10238581
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Simplified Design of Magnetic Gear by Considering the Maximum Transmission Torque Line

Abstract: Magnetic gears (MGs) technology is studied widely among research institutions, with several improvements being documented. This development attracts a high amount of attention due to the demand in the development of magnetic gears towards higher performance than the conventional mechanical counterpart. In general, the design is complicated and there is a lack in detailed references for designing an MG for specific transmission torque as required by its application. Trial-and-error approaches have been the norm… Show more

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Cited by 9 publications
(6 citation statements)
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“…The use of magnetic gearboxes is promising [22,23]. They make it possible to abandon the mechanical gearbox in favor of a magnetic gearbox and use a classic highspeed and more compact, cheap, affordable, and easytomaintain electric generator on the output shaft of the wind turbine [24,25].…”
Section: Electrical Complexes and Systemsmentioning
confidence: 99%
“…The use of magnetic gearboxes is promising [22,23]. They make it possible to abandon the mechanical gearbox in favor of a magnetic gearbox and use a classic highspeed and more compact, cheap, affordable, and easytomaintain electric generator on the output shaft of the wind turbine [24,25].…”
Section: Electrical Complexes and Systemsmentioning
confidence: 99%
“…Structure of surface-mount CMG [10] Since the demand for high efficiency and compact gears has increased, hence also the need to accommodate devices that incorporate miniscule and energy efficiency in their design. The presence of highenergy-density PM in CMG contributes to a large increase in eddy current loss in magnetic gears, particularly during high-speed operations, and lowering gear efficiency [13]- [16]. A reduction in gear efficiency weakens torque capability, hence rendering for lower torque density.…”
Section: Introductionmentioning
confidence: 99%
“…It has been gradually used for complex data fitting with the continuous development of data-driven methods [16]- [21]. Both supervised and unsupervised learning approaches have been applied to magnetic field approximation [14], [22]- [25].…”
Section: Introductionmentioning
confidence: 99%
“…So far, a series of AI and DL concepts have been proposed, including deep neural networks with fully connected networks (FCN), convolutional networks (CNN), and generative adversarial networks (GAN) [14], [26], [27].…”
Section: Introductionmentioning
confidence: 99%