MétaCan
Menu
Back to cohort
Record W2621159571 · doi:10.1109/tmag.2017.2711605

Field-Weakening Capability of Interior Permanent-Magnet Machines With Salient Pole Shoe Rotors

2017· article· en· W2621159571 on OpenAlexaff
Nan Zhao, N. Schofield

Bibliographic record

VenueIEEE Transactions on Magnetics · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMagnetTraction (geology)TorqueComputer scienceAutomotive engineeringTopology (electrical circuits)Power (physics)Traction motorMechanical engineeringControl theory (sociology)Electrical engineeringEngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Brushless permanent-magnet (BLPM) machines, with inherent advantages of high-power density and high efficiency, have been widely employed to achieve traction characteristics for traction applications. Generally, traction characteristics require high torque at low speed and wide field-weakening region keeping constant power. However, both conventional interior permanent-magnet (IPM) and surface-mounted permanent-magnet (SPM) machines suffer from high-speed issues in the field-weakening region. A different BLPM machine topology, the pole shoe topology, is proposed in this paper. Although the pole shoe machine is common in industrial variable speed drives employing constant torque regimes, it has not been previously considered for machines designed for a wide field-weakening region. For analysis and comparison, a conventional IPM machine, which is employed as the Nissan Leaf vehicle traction machine, is studied as a reference benchmark machine. Experimental results from this machine are used to validate the analysis presented in this paper. The design results show that the proposed pole shoe machine achieves better field-weakening performance, compared with the conventional IPM and SPM machine topologies.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.622

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 head0.008
GPT teacher head0.220
Teacher spread0.211 · 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 designOther design
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

Citations20
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on MagneticsSame topicElectric Motor Design and AnalysisFrench-language works237,207