A Neural-FEM tool for the 2-D magnetic hysteresis modeling
Physica B: Condensed Matter, ISSN: 0921-4526, Vol: 486, Page: 111-115
2016
- 31Citations
- 9Captures
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Article Description
The aim of this work is to present a new tool for the analysis of magnetic field problems considering 2-D magnetic hysteresis. In particular, this tool makes use of the Finite Element Method to solve the magnetic field problem in real device, and fruitfully exploits a neural network (NN) for the modeling of 2-D magnetic hysteresis of materials. The NS has as input the magnetic inductions components B at the k -th simulation step and returns as output the corresponding values of the magnetic field H corresponding to the input pattern. It is trained by vector measurements performed on the magnetic material to be modeled. This input/output scheme is directly implemented in a FEM code employing the magnetic potential vector A formulation. Validations through measurements on a real device have been performed.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0921452615303392; http://dx.doi.org/10.1016/j.physb.2015.12.006; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84959509589&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0921452615303392; https://api.elsevier.com/content/article/PII:S0921452615303392?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0921452615303392?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.physb.2015.12.006
Elsevier BV
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