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Record W2795416329 · doi:10.1139/tcsme-2015-0034

THE DESIGN AND MESHING EFFICIENCY ANALYSIS OF HELICAL SPUR GEAR REDUCER WITH SINGLE GEAR PAIR FOR ELECTRIC SCOOTERS

2015· article· en· W2795416329 on OpenAlexvenueno aff
Long–Chang Hsieh, Tzu-Hsia Chen, Hsiu-Chen Tang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersNational Science Council
KeywordsReducerPinionGear ratioStructural engineeringSpurEngineeringNon-circular gearAutomotive engineeringSpiral bevel gearRotation (mathematics)Mechanical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

To achieve reduced costs and energy conservation, this paper proposes non-standard helical spur gear reducer with one gear pair (having reduction ratio 19.25) to be the gear reducer for electric scooter. This paper also focuses on the meshing efficiency analysis of non-standard helical spur gear pair. According to Buckingham’s research, the theoretical meshing efficiency formula of non-standard helical spur gear pair is derived. Three design cases of non-standard helical spur gear pair (4, 77) are proposed as examples for analyzing their meshing efficiencies at widely rotation speed range. The theoretical meshing efficiencies for the helical spur gear pair (4, 77) are between 96.47–99.26%. Its best meshing efficiency occurs at 800–1000 rpm of pinion. The meshing efficiencies of these three design cases are almost same, and their differences are less than 0.5%. Considering the root strength of pinion, Cases II and III are better than Case I.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.192
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

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