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Record W1989982629 · doi:10.1109/tia.2003.821794

Transient Performance Analysis for Permanent-Magnet Hysteresis Synchronous Motor

2004· article· en· W1989982629 on OpenAlexaff
K. Kurihara, Mohammad Azizur Rahman

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2004
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTransient (computer programming)HysteresisControl theory (sociology)Synchronous motorMagnetNonlinear systemRotor (electric)Magnetic hysteresisFinite element methodPermanent magnet synchronous motorAC motorComputer scienceMaterials scienceElectric motorEngineeringPhysicsMechanical engineeringStructural engineeringElectrical engineeringMagnetic fieldCondensed matter physics

Abstract

fetched live from OpenAlex

The combination of hysteresis and permanent-magnet materials in the rotor of a self-starting synchronous motor makes the motor analysis very difficult due to its inherent nonlinearity. This paper presents the simulation results of the transient performance of permanent magnet hysteresis synchronous (PMHS) motors. The major feature in this study is to combine the time-stepping finite-element technique with the model for B-H hysteresis loop in order to take the nonlinear magnetic hysteresis into account. The good agreement between computed and measured performance in a laboratory PMHS motor validates the proposed analysis.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.244
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations25
Published2004
Admission routes1
Has abstractyes

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