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Record W4411232402 · doi:10.1109/tia.2025.3579415

Experimental Validation of a Spoke Type PMSM With SMC Stator Core for Traction Applications

2025· article· en· W4411232402 on OpenAlexaff
Mohanraj Muthusamy, Mathews Boby, Akrem Mohamed Aljehaimi, James Hendershot, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia UniversityOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsStatorTraction (geology)Traction motorCore (optical fiber)Control theory (sociology)Computer scienceElectrical engineeringEngineeringAutomotive engineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents the design, analysis and testing of a soft magnetic composite (SMC) based stator using a newly developed SMC material with a novel spoke-type rotor. Firslty, this paper analyses four different slot/pole configurations for surface permanent magnet (PM) machines with laminated and SMC stator cores, proving that the SMC material can be used to design a traction motor. Secondly, this paper develops a technique for eliminating the hub arrangement for a spoke-type rotor with a novel air barrier, along with improving the electromagnetic performances. Additionally, the electromagnetic performance is compared for three different rotor designs (Design A,B and C) with the same specifications. Design-C emerged as the best candidate based on the coupled multi-objective optimization with electromagnetic and structural analysis, as well as its manufacturability. Finally, the fabrication and experimental results for the Design-C rotor with SMC stator core are presented.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.001

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 head0.020
GPT teacher head0.276
Teacher spread0.255 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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