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Record W2746600295 · doi:10.11159/ffhmt17.169

Mechanical Buckling Induced Thermoelastic Instability in Frictional Clutches

2017· article· en· W2746600295 on OpenAlexvenueno aff
Heyan Li, Mingyang Li, Biao Ma, Jikai Liu, Liang Yu, Huizhu Li

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicBrake Systems and Friction Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsThermoelastic dampingBucklingClutchMaterials scienceInstabilityStructural engineeringMechanicsMechanical engineeringComposite materialEngineeringThermalPhysicsThermodynamics

Abstract

fetched live from OpenAlex

A multi-disc clutch test bench was set up and sliding experiments were conducted to investigate the relationship between the friction glazed spot distribution and mechanical torsion buckling on frictional components.The buckling deformation model of a separator disc with spline teeth subjected to mechanical torsion is established to compare with the experimental observation.The buckled spline disc provides a certain perturbation frequency during the sliding period.Under this sliding condition, when the relative sliding rubbing speed in frictional clutches exceeds the thermoelastic instability (TEI) speed predicted by Barber and Lee's TEI model, mechanical buckling induces thermoelastic instability (MBTEI).The dangerous sliding speed and perturbation pressure growth rate are obtained in regard to structural parameters, temperatures, abrasion cracks and torques.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.239
Teacher spread0.206 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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