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Record W2514009634 · doi:10.1109/tec.2016.2600178

Double Segmented Rotor Switched Reluctance Machine With Shared Stator Back-Iron for Magnetic Flux Passage

2016· article· en· W2514009634 on OpenAlexafffund
Guo Teng, N. Schofield, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSwitched reluctance motorPowertrainRotor (electric)Magnetic reluctanceStatorReluctance motorMagnetEngineeringElectric machineComputer scienceAutomotive engineeringControl theory (sociology)TorqueMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

There is much interest in double rotor electric machines due to their versatile configurations and performance characteristics arising from the flexibility of having a pair of rotors. Double rotor machines show promising application prospects in advanced hybrid electric vehicle powertrains due to the requirement of dual electromechanical ports in such systems. Integrating these powertrain systems with double rotor machines not only brings design freedom in component layout, but also reduces the number of parts and thus improves compactness. The switched reluctance family of double rotor machines offers unique characteristics in terms of simple structure and no permanent magnets and thus is a strong candidate for such applications. By introducing the segmented rotor design to double rotor switched reluctance machines, a unique design with shared stator back-iron is made possible, achieving increased machine compactness. This paper discusses the design, analysis, and experimental validation of a double segmented rotor switched reluctance machine and compares the design against a target double rotor SRM specification.

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 categoriesInsufficient payload (model declined to judge)
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 score0.983
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.185
Teacher spread0.177 · 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.

Study designBench or experimental
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

Citations26
Published2016
Admission routes2
Has abstractyes

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