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Record W2011959031 · doi:10.1109/tmag.2014.2350508

Improved Analytical Model for Predicting the Magnetic Field Distribution in High-Speed Slotless Permanent-Magnet Machines

2015· article· en· W2011959031 on OpenAlexaff
Ahmed Chebak, P. Viarouge, J. Cros

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité LavalUniversité du Québec à Rimouski
Fundersnot available
KeywordsMagnetStatorEddy currentElectromagnetic coilMagnetic fieldRotor (electric)Finite element methodMagnetic reluctanceElectrical conductorMagnetic coreMechanicsMechanical engineeringTorqueMaterials sciencePhysicsNuclear magnetic resonanceEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper presents a general analytical 2-D model for computing the magnetic field distribution in high-speed slotless surface-mounted permanent-magnet machines. This model is suitable for machines equipped with soft magnetic composite or laminated steel stators and with retaining sleeves that can be conductive or non-conductive and magnetic or non-magnetic. The analytical model considers the contribution of magnetic fields produced by the rotor magnets, stator windings currents, and eddy currents induced in the conductive regions in the stator and rotor. The computation accounts for the relative recoil permeabilities of the magnets, retaining sleeve, and stator core material. The analytical model is validated by 2-D finite element analysis and used to compute and analyze the electromagnetic torque components due to the different magnetic field sources interactions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.230
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

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