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Record W2587693299 · doi:10.1109/tte.2017.2664778

Three-Phase 24/16 Switched Reluctance Machine for a Hybrid Electric Powertrain

2017· article· en· W2587693299 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 Transportation Electrification · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSwitched reluctance motorStatorReluctance motorTorque ripplePowertrainControl theory (sociology)Rotor (electric)TorqueDirect torque controlElectric vehicleTraction motorAutomotive engineeringEngineeringComputer scienceInduction motorVoltageMechanical engineeringPhysicsElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

This paper presents the design process of a 60-kW switched reluctance motor for traction application in a hybrid electric vehicle. The motor has 24 stator poles and 16 rotor poles. A multiobjective optimization method has been employed in the optimization of conduction angles for priority operating points and over the entire operating range. Two objectives, maximizing output torque and minimizing torque ripple, are used in the optimization problem. A comprehensive comparative performance analysis with varying stator pole height, stator taper angle, rotor pole arc angle, and pole shoe shape is also presented. The performance of the proposed motor over the entire operating range has been characterized based on the optimized conduction angles. The finite element analysis results of the machine demonstrate the potential of the proposed motor in hybrid powertrains in terms of output torque, torque quality, and efficiency.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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