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Analysis of Thermoelastic Instability on Multi-Disc Clutch

2011· article· en· W2132058557 on OpenAlexaff
Wei Yang, Lu Guo, He Sheng Lv

Bibliographic record

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicBrake Systems and Friction Analysis
Canadian institutionsOntario Drive & Gear (Canada)
Fundersnot available
KeywordsThermoelastic dampingClutchInstabilityMechanicsDisc brakeStructural engineeringMaterials sciencePhysicsEngineeringBrakeThermodynamicsThermalMetallurgy

Abstract

fetched live from OpenAlex

Thermoelastic instability of multi-disc clutch contributes to the failure of power transmission and as such multi-disc clutch is considered as a research object with a theory model built on perturbation method. The characteristic equations on the thermoelastic instability of geometrically symmetrical and antisymmetrical steel-disc are derived. The results are shown that both wave number and disc thickness have significant influences on the thermoelastic instability of the steel-disc. Choosing a larger wave number or a thicker steel-disc can prevent the thermoelastatic instability state in multi-disc clutch. Compared with the geometrically symmetrical steel-disc,the geometrically antisymmetrical steel-disc is more susceptible to thermoelastic instability.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.025
GPT teacher head0.203
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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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