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Record W2580045180 · doi:10.1109/iemdc.1999.769226

Synthesis of high performance PM motors with concentrated windings

2003· article· en· W2580045180 on OpenAlexaff
J. Cros, P. Viarouge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsElectromagnetic coilJoule (programming language)Three-phasePower (physics)Computer scienceElectrical engineeringPhase (matter)Topology (electrical circuits)Mechanical engineeringEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Windings concentrated around the teeth offer obvious advantages for electric machines with radial air-gaps, because the volume of copper used in the end-windings can be reduced. The Joule losses are decreased and the efficiency is improved. These machines are still limited to applications of sub-fractional power and they generally present a reduced number of phases. In three-phase machines, the concentrated winding is too often restricted to a winding with a short pitch of 120 electric degrees, i.e. to a winding with performances reduced compared to the traditional structures. But there are a significant number of three-phase structures which can support a concentrated winding if the number of poles is increased. In this article, the author present a synthesis of the structures of three-phase machines with concentrated windings. In the first part, the structures with a regular distribution of the slots are presented. A systematic method is proposed to determine the windings and the performances are discussed. In the second part, the authors present original structures of three-phase machines with concentrated windings which use an irregular distribution of the slots. A specific method to identify these structures is described and a comparative analysis of the performances of the original and traditional structures is performed by using field calculation software.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.351

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 head0.004
GPT teacher head0.157
Teacher spread0.153 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

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