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Record W1530495152 · doi:10.4271/2004-01-1346

Finite Element Study of Belt-Drive Frictional Contact under Harmonic Excitation

2004· article· en· W1530495152 on OpenAlexaff
Michael J. Leamy, R. J. Meckstroth, Tamer M. Wasfy

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsFinite element methodExcitationHarmonicHarmonic analysisPhysicsStructural engineeringMechanicsEngineeringAcousticsElectrical engineeringElectronic engineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Belt-drives that are driven by internal combustion engines, such as automotive accessory drives, are subjected to high-frequency periodic excitation due to engine combustion events. This excitation is propagated from the engine through the crank pulley and the belt to the accessory pulleys and may cause unwanted system behavior, to include 1) excessive creep or gross slip of the belt on one or more pulleys, 2) large belt-span transverse vibrations, 3) and/or large belt-span tension variations. In turn, the excitation may be responsible for premature degradation and failure of the belt or other components of the belt-drive.</div> <div class="htmlview paragraph">In this paper, an explicit time integration finite element code is used to study the effect of harmonic excitation on the belt-drive operation of a prototypical two-pulley belt-drive. In particular, the study documents the effect of excitation frequency and magnitude on: the belt tension and friction forces along the contact arc, the extent of belt creep or gross slip on the pulleys, the angular velocity of the driven pulley, and the belt-span tension fluctuations. A previously developed closed-form approximate analytical solution that is valid over a limited range of excitation frequencies and amplitudes is used to validate the finite element solution.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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 designObservational
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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

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