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Record W3048660292 · doi:10.1109/tia.2020.3015693

Effect of Skewing in a Variable Flux Interior Permanent Magnet Synchronous Machine

2020· article· en· W3048660292 on OpenAlexafffund
Dwaipayan Barman, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsCogging torqueTorque rippleCounter-electromotive forceMagnetControl theory (sociology)TorqueRotor (electric)RippleFinite element methodDirect torque controlMagnetizationPhysicsMaterials scienceEngineeringCurrent (fluid)Computer scienceMechanical engineeringElectrical engineeringStructural engineeringVoltageMagnetic fieldInduction motor

Abstract

fetched live from OpenAlex

The motivation of this article is to minimize the cogging torque and the torque ripple in a 6-pole 27-slot variable flux interior permanent magnet synchronous machine (VF IPMSM) by skewing the permanent magnets (PMs) in several steps. AlNiCo9 is used as the PM material in the rotor as the magnetization level of the AlNiCo9 PM can be changed and controlled by proper current control. The optimum skewing angles to minimize the cogging torque are found analytically and verified by using the finite-element analysis (FEA). The effect of these skewing angles on the back electromotive force and the torque ripple is also studied at different magnetization levels of the AlNiCo9 magnets. An FEA shows that a step skewed PM pole significantly minimizes the cogging torque and the torque ripple in the VF IPMSM. A current pulse in the d-axis is applied to magnetize or demagnetize each step in the step skewed PM pole. The minimum optimum skewing angle is chosen such that each step of the step skewed PM pole is magnetized or demagnetized with minimum nonuniformity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.218
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 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

Citations20
Published2020
Admission routes2
Has abstractyes

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