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Numerical Simulation of Stresses Produced on Hydraulic Clutch Discs due to Heat Generated During Operation

2021· article· en· W3189733313 on OpenAlexaff
Estaner Claro Romão, Ravinder Janghu

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2021
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClutchSlippingFinite element methodvon Mises yield criterionTorqueStress (linguistics)Mechanical engineeringHeat transferThermalWork (physics)MechanicsMaterials scienceStructural engineeringEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Modern clutches are a critical component of a machine/equipment because they are designed to transfer and control torque, consequently producing movements, enabling safe operation, and controlling movement when necessary. When a clutch engages it transfers torque and allows a machine to produce mechanical work as a final result. The clutch engagement process generates heat due to the slipping of clutch discs. Although pressure applied on clutch discs generates stresses in the disc material, it was not clear how severe a combination of pressure and heat might raise the stresses. This study aimed to produce a numerical simulation to determine the impact of temperature also torque changes in the clutch discs when it transfers movement. Some static and thermal numerical simulations by Finite Element Analysis (FEA) (linear-elastic analysis) were performed, which considered two scenarios; (1) first with only pressures applied on clutch disc's face; (2) where heat was added to pressures. These mathematical simulations revealed that discs stresses are highly sensitive to thermal variations since for some cases the maximum von Mises stresses exceeded discs material mechanical strength leading it to failure. To overcome this problem it is compulsory to consider heat when designing a clutch and a cooling system for it.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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 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
Published2021
Admission routes1
Has abstractyes

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Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicTribology and Lubrication EngineeringFrench-language works237,207