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Record W2109351365 · doi:10.1109/tec.2014.2378211

Double-Rotor Switched Reluctance Machine (DRSRM)

2015· article· en· W2109351365 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsSwitched reluctance motorTorqueMagnetic reluctanceStatorElectric machineRotor (electric)Traction (geology)Torque densityFinite element methodAutomotive engineeringReluctance motorElectric vehicleEngineeringProcess (computing)Computer scienceElectromagnetic coilControl engineeringMechanical engineeringMagnetElectrical engineeringPower (physics)Structural engineering

Abstract

fetched live from OpenAlex

With the era of modern vehicle electrification, electric machines with high traction torque-speed output and compact volume are highly desired. This paper presents a family of switched reluctance machine configurations that are composed of double rotors and one stator integrated in one machine housing. The machines are potentially more compact and lower cost, while providing two independent mechanical outputs suitable for hybrid electric vehicle transmissions. The detailed design process of a double-rotor switched reluctance machine (DRSRM) is presented, comprising of analytical calculations, finite-element field analysis, and full-drive system simulation. Additionally, some optimizations are applied to maximize the machine performance and minimize the machine weight and volume. A scaled prototype machine is designed and built according to chosen vehicle drive cycles to evaluate and validate the DRSRM prototype.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.756

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 head0.016
GPT teacher head0.206
Teacher spread0.190 · 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