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Record W2948107111 · doi:10.11159/ffhmt19.116

The Wear Characteristics of Wet Clutch in the DCT Vehicle During the Launching Process

2019· article· en· W2948107111 on OpenAlexvenueno aff
Qianqian Zhang, Man Chen, Biao Ma, Jianpeng Wu, Liang Yu

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsClutchProcess (computing)Discrete cosine transformAutomotive engineeringComputer scienceEngineeringArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

In order to investigate the wear characteristics of wet clutch during the vehicle launching process, the experimental investigations and the dynamic analysis are conducted in the study. Firstly, the wet pin-on-disc experiment is designed to explore the wear behaviours of the wet friction pair. Based on the experimental results under various conditions, the wear amount is measured by the multielement oil analysis spectrometer and the corresponding wear coefficient is deduced according to the Archard laws. After that, the dynamic model of the Double Clutch Transmission (DCT) vehicle is established to study the wear condition of the wet clutch during the vehicle launching process. The experimental and simulation results indicate that, the increase of the slipping speed, the contact pressure and the temperature leads to the increase of the wear amount, while, for the wear coefficient, the slipping speed has little influence. During the vehicle launching process, the driver's gentle operation with light vehicle load will reduce the wear amount of the wet clutch and prolong its service life.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicMechanical stress and fatigue analysisFrench-language works237,207