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Record W3094845794 · doi:10.1109/cjece.2020.3002243

A Concept of Accelerating the Demagnetization of Switched Reluctance Motor

2020· article· en· W3094845794 on OpenAlexvenueno aff
Jiayi Fan, YongKeun Lee

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
FundersSeoul National University of Science and Technology
KeywordsDemagnetizing fieldSwitched reluctance motorInductanceTorqueElectromotive forceControl theory (sociology)Counter-electromotive forceComputer scienceCurrent (fluid)PhysicsVoltageEngineeringElectrical engineeringMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

The demagnetization of switched reluctance motor (SRM) is usually a nonnegligible problem as the speed increases due to large back electromotive force (EMF). A slow demagnetization causes long tail current and negative torque, which reduces the efficiency and the output capability of SRM. In the conventional SRM design method, the inductance slope is normally designed to be high. However, it causes the problems of slow demagnetization in the negative torque region and low utilization efficiency of the torque-generative region. This article proposed a totally contrary way of SRM design, a small value of the inductance slope is chosen. Therefore, the effect of the back EMF can be diminished and the demagnetization is improved. The utilization of the torque-generative region is also improved due to the delayed turn-off angle. Simulation results are provided to verify the advantages of the proposed idea compared with the conventional one.

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 score0.637
Threshold uncertainty score0.273

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 head0.160
Teacher spread0.152 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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