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Record W1922932301 · doi:10.1109/ecce.2015.7310577

A novel grain oriented lamination rotor core assembly for a synchronous reluctance traction motor with reduced torque ripple

2015· article· en· W1922932301 on OpenAlexaff
Seyedmorteza Taghavi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsTorque rippleCogging torqueReluctance motorSwitched reluctance motorLaminationTorque densityTorqueDirect torque controlRotor (electric)Synchronous motorMechanical engineeringMagnetic reluctanceTraction motorMaterials scienceTraction (geology)EngineeringAutomotive engineeringComputer scienceMagnetElectrical engineeringPhysicsInduction motorComposite material

Abstract

fetched live from OpenAlex

High torque density and low torque ripple are crucial for traction applications. These allow the electrified powertrains to perform properly during start-up, acceleration, and cruising. Achieving these goals requires improvement to the saliency of the rotor geometry which means higher magnetization in the flux carriers and lower magnetization through the flux barriers. Recent advantages of high quality anisotropic magnetic materials such as cold rolled grain oriented electrical steels is a potential for achieving energy efficient, compact, and high performance synchronous reluctance machines. However, the cylindrical geometry of the rotor is an obstacle to utilizing these materials for rotor lamination with number of poles higher than two. This paper presents an innovative rotor lamination design and assembly using cold rolled grain oriented electrical steel along with a new analytical approach for rotor flux barrier design for achieving a higher torque density and lower torque ripple for a 4-pole synchronous reluctance motor. The design methods and prototyping process are discussed, finite element analyses and experimental examinations are carried out and the results are compared to verify and validate the proposed methods.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.584

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 head0.020
GPT teacher head0.236
Teacher spread0.216 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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