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Record W3025972542 · doi:10.1109/tvt.2020.2993725

Making the Case for Switched Reluctance Motors for Propulsion Applications

2020· article· en· W3025972542 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSwitched reluctance motorPropulsionTorque rippleAutomotive engineeringElectrically powered spacecraft propulsionEngineeringReluctance motorTorqueElectric motorControl engineeringComputer scienceDirect torque controlElectrical engineeringInduction motorRotor (electric)Aerospace engineeringVoltage

Abstract

fetched live from OpenAlex

This paper makes the case that Switched Reluctance Motor (SRM) is an exceptionally attractive technology to respond to the increasing demand in propulsion applications for high-efficiency, high-performance, and low-cost motors with a more secure supply chain. The paper also presents methods to effectively reduce torque ripple and acoustic noise, which have been the major issues impeding the widespread use of SRMs. Finally, SRM designs for two propulsion motors, one for an e-bike and the other for a hybrid electric vehicle application, are presented which have been proposed to replace permanent magnet machines.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.473

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 integrity0.0000.000
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 head0.021
GPT teacher head0.246
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