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Design and analysis of cobra shaped spoke type rotor with SMC stator core for traction applications

2022· article· en· W4310475748 on OpenAlexaff
Mohanraj Muthusamy, Pragasen Pillay

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsStatorRotor (electric)Core (optical fiber)Traction (geology)TorqueTorque densityTorque rippleTraction motorMagnetic coreEngineeringAutomotive engineeringMechanical engineeringCopper lossElectric motorInduction motorElectrical engineeringDirect torque controlElectromagnetic coilPhysicsVoltageTelecommunications

Abstract

fetched live from OpenAlex

Magnetic materials are envisaged as a key factor for the performance improvement in electric traction motors. This paper focuses on reducing the torque ripple, and the harmonic content of back EMF utilizing the cold spray additively manufactured magnets. The electromagnetic performances of the conventional spoke and the cobra-shaped spoke type rotor are compared for the same specifications. Also, this paper focuses on the design of a segmented SMC stator core to improve the copper fill factor to 60%. A comparison between the laminated stator core and the SMC stator core has been presented. The tooth body of the SMC stator core is reduced on both sides to compact the end winding within the stack height, increasing the torque density in terms of motor volume.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2022
Admission routes1
Has abstractyes

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