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Record W1587612741 · doi:10.1109/iecon.2014.7049068

A comparative study of synchronous reluctance machine performance with different pole numbers for automotive applications

2014· article· en· W1587612741 on OpenAlexafffund
Seyedmorteza Taghavi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomotive industryMagnetic reluctanceTorqueSizingTorque rippleSwitched reluctance motorReluctance motorTraction (geology)Rotor (electric)Magnetic gearSynchronous motorPoint (geometry)Automotive engineeringComputer scienceEngineeringDirect torque controlMechanical engineeringMagnetElectrical engineeringInduction motorMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents a comparative study on the effects of the number of poles on the synchronous reluctance machine's magnetic and mechanical performances for automotive applications. In automotive applications the design limitations i.e., proper size, maximum speed, torque envelope, and converter rating make the design sensitive to magnetic and geometric parameters such as the number of poles. Low torque ripple requirement for traction motors leads to the number of poles higher than two. From a rotor geometry point of view the number of poles higher than six would not be feasible in today's compact traction motors. Two different motor alternatives using 4 and 6 poles have been designed using a sizing methodology. In this work, machines equipped with transversally laminated anisotropic rotors. Magnetic and mechanical performances of the machines are analyzed and compared with regards to the design requirements. Finally, the proper design alternative will be addressed for automotive applications.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations19
Published2014
Admission routes2
Has abstractyes

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