MétaCan
Menu
Back to cohort
Record W2497262798 · doi:10.1109/itec.2016.7520232

Noise and electromagnetic comparison of a three-phase 12/8 and a 12/10 switched reluctance machine

2016· article· en· W2497262798 on OpenAlexafffund
Earl Fairall, N. Schofield, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsSwitched reluctance motorInductanceTorquePhase (matter)Magnetic reluctanceTopology (electrical circuits)Control theory (sociology)VibrationNoise (video)Work (physics)Magnetic fluxReluctance motorComputer sciencePhysicsEngineeringElectrical engineeringAcousticsMagnetMagnetic fieldMechanical engineering

Abstract

fetched live from OpenAlex

In this work, non-segmented three-phase 12/10 magnetic topology is compared with a three-phase 12/8 design. While higher pole count often implies higher losses, the 12/10 functions entirely on short flux paths. 12/10 core loss implications are; therefore, not immediately clear when compared with 12/8. Pole count also impacts inductance and saliency, parameters which govern all aspects of switched reluctance machine performance. To better understand the non-segmented three-phase 12/10, a comparison is presented with geometrically and magnetically similar three-phase 12/8 and 12/10 switched reluctance machines. The influence of pole count on losses, torque quality, noise and vibration will be compared under specific defined conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207