Methods of Estimating AC Losses in Superconducting MgB Armature Windings With Spatial and Time Harmonics

Recent developments in low ac loss MgB_{2} conductors are of significant importance given renewed interest in fully superconducting (SC) machines. Evaluating ac losses in fully SC machines is a critical step in developing feasible designs. In fully SC machines, SC armature windings experience non-un...

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Bibliographic Details
Published in:IEEE transactions on applied superconductivity 2022-09, Vol.32 (6), p.1-7
Main Authors: Balachandran, Thanatheepan, Salk, Noah J., Lee, Dongsu, Sumption, M. D., Haran, Kiruba S.
Format: Article
Language:English
Subjects:
Ac loss
Analytical models
Coils (windings)
Conductors
Environment models
Estimation
finite element analysis
Finite element method
Harmonic analysis
Harmonics
Magnetic fields
Magnetic hysteresis
Multifilamentary superconductors
Nonuniform magnetic fields
power law
Rotation
superconducting machines
Superconductivity
Windings
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Summary:Recent developments in low ac loss MgB_{2} conductors are of significant importance given renewed interest in fully superconducting (SC) machines. Evaluating ac losses in fully SC machines is a critical step in developing feasible designs. In fully SC machines, SC armature windings experience non-uniform rotating magnetic fields, with spatial and temporal harmonics, which has an undisputed impact on ac losses. Existing ac loss models in the literature, which have been validated for stationary sinusoidal external fields, were extended to constant amplitude rotating fields. There is not enough research on validating the ac loss models for rotating non-uniform magnetic fields with harmonics. This paper proposes simplified methods to estimate the ac losses in conductors with non-uniform rotational applied magnetic fields experienced by the armature in a machine's environment. Extended analytical models are proposed to estimate the ac loss in single and multi-filament MgB_{2} conductors. The models are then compared against finite element analysis (FEA) results with Power Law loss estimation to evaluate model fidelity.
ISSN:1051-8223
1558-2515
DOI:10.1109/TASC.2022.3181535