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Record W2948308475 · doi:10.11159/ffhmt19.120

Modelling and Simulation of Lubricant Flow Fluid in Wet Friction Pair

2019· article· en· W2948308475 on OpenAlexvenueno aff
Liyong Wang, Qian Wang, Le Li

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLubricantFlow (mathematics)MechanicsFluid dynamicsComputer scienceMaterials scienceMechanical engineeringPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

The lubricant flow field between the disks of the wet friction pair has been simulated under different lubricant inlet flow rate. The temperature changing on the surfaces of the disks during the wet friction pair operation is investigated. The temperature curve which was exported from the thermo-mechanical coupling simulation for the wet friction pair, is used to define the thermal field of the lubricant flow calculation filed. The viscosity-temperature characteristics of the lubricant is considered in this research as well. The simulation results of the lubricant flow fluid based on the different temperature and different flow velocity distribution show that grooves effect is the very important factor of the lubricant flow fluid. It is found that the flow velocity in the radial grooves and the zone near the walls contact to friction disk which has been grooved is larger while the temperature is lower. It is also shown that the temperature field and flow velocity field are distributing circularly and the closer to the outer edge the higher the flow rate and the higher the temperature is.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.420

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 science0.0000.000
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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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 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
Published2019
Admission routes1
Has abstractyes

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