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3D modeling of stack-type superconducting magnetic bearings

Authors: Ghabeli, Asef; Grilli, Francesco; Hänisch, Jens; Fuchs, Günter; de Haas, Oliver; Zhou, Pengbo;

3D modeling of stack-type superconducting magnetic bearings

Abstract

Intrinsically stable levitation force in a superconducting magnetic bearing (SMB) makes it an appealing technology to be employed in transportation industry and in particular in maglev vehicles. SMBs made of stacks of HTS tapes possess some practical features to compete with the superconducting bulks by offering better mechanical properties, better heat conductivity and simpler production process. Modeling can be employed as a powerful, cost-effective and fast tool to understand the physical mechanisms, interpretation of experimental results, and to predict and improve the performance of SMBs. Although in the past several 2D models have been proposed and verified against experiments, there is lack of stable and fast 3D models to analyze the performance of SMBs. In this work, we developed an open-source software coded in C++ to analyze the performance of a stack-type SMB, and verified our modeling results through comparison with measurements in field cooling and zero field cooling modes at different temperatures, from 77 K to 59 K. Contrary to previously presented models, we used the Jc(B,θ) measurement data to describe the characteristics of the HTS tapes in the stacks. This eliminates the need of artificially calibrating the model using the maximum levitation force to obtain the hysteresis force graph. However, this requires some considerations in using Jc(B,θ) measurement data and implementing it to the model.

Country
Germany
Related Organizations
Keywords

Physics, ddc:530, info:eu-repo/classification/ddc/530, 530

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green