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Record W2903222608 · doi:10.1139/tcsme-2017-510

DEVELOPMENT OF MAGNETIC GEAR MECHANISMS

2017· article· en· W2903222608 on OpenAlexvenueno aff
Yi-Chang Wu, Chin-Tsung Chan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetic gearSchematicFlowchartTorqueMagnetMagnetic fieldMagnetic reluctanceMechanism (biology)Computer scienceRepresentation (politics)Mechanical engineeringFinite element methodTopology (electrical circuits)EngineeringPhysicsElectrical engineeringStructural engineering

Abstract

fetched live from OpenAlex

The purpose of this study is to develop novel magnetic gear mechanisms by using creative mechanism design methodology. First, the flowchart of creative mechanism design methodology is introduced. The schematic diagram and graph representation of a magnetic gear device based on its topological structure are presented. Next, the characteristics of the topological structure of an existing 4-link magnetic gear device are analyzed. Feasible specialized chains are then enumerated, subject to design requirements and constraints. The direction of magnetization in permanent magnets is defined and applied to magnetic gear mechanisms. Finally, 12 novel magnetic gear mechanisms with 5 links are synthesized. The magnetic field, transmitted torque and speed ratio of a novel design are analyzed by 2-D FEA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.613

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 head0.011
GPT teacher head0.192
Teacher spread0.180 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

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