Large-Scale Micromagnetics Simulation of Magnetization Dynamics in a Permanent Magnet during the Initial Magnetization Process

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Tsukahara ◽  
Kaoru Iwano ◽  
Tadashi Ishikawa ◽  
Chiharu Mitsumata ◽  
Kanta Ono

2015 ◽  
Vol 15 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Andris Bojarevičs ◽  
Toms Beinerts ◽  
Mārtiņš Sarma ◽  
Yurii Gelfgat

AbstractMultiple configurations of synchronously rotating permanent magnet cylinders magnetized across the axes are proposed for liquid metal stirring for homogenization as well as for pumping. Universal analytical model is used for an initial parameter analysis. Then experimental setups were built to perform physical modelling of the industrial applications, e.g. large-scale metallurgical furnaces. Velocity distribution in the liquid metal was measured using different methods: the Ultrasound Doppler anemometry and the potential difference probes. The study shows that the cylindrical permanent magnet setups can achieve up to 10 times higher energy efficiency compared to AC inductors and have potential of wide-range industrial application, e.g. can be used as stirrers for secondary aluminium furnaces with up to 50 cm thick walls.





2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chin-Tsung Hsieh ◽  
Her-Terng Yau ◽  
Jen Shiu

This study proposes a method based on the cerebellar model arithmetic controller (CMAC) for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP). The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.





AIChE Journal ◽  
2014 ◽  
Vol 60 (9) ◽  
pp. 3101-3106 ◽  
Author(s):  
Wensong Li ◽  
Liangrong Yang ◽  
Huizhou Liu ◽  
Xiaopei Li ◽  
Zhini Liu ◽  
...  


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