MétaCan
Menu
Back to cohort
Record W4390029663 · doi:10.18280/mmep.100632

Effect of Double Porous Layer on Rough Step Slider Bearing Lubricated with Couple Stress Fluid

2023· article· en· W4390029663 on OpenAlexvenueno aff
Johny Anthony, Sujatha Elamparithi

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceBearing (navigation)Stress (linguistics)SliderPorosityComposite materialLayer (electronics)Mechanical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

An investigation into how a double porous layer at the base impacts the performance of a Rayleigh step slider bearing lubricated with couple stress fluid is presented.The bearing is considered to be rough in nature.The modified Darcy's law is used to express the fluid pressure in the porous regions.To estimate the effect of surface roughness of the bearing the random variables pertaining to roughness parameters are analysed applying the Christen'S stochastic model.Applying Stokes' micro-continuum theory for couple stress yields an estimation of pressure via a modified Reynolds equation.Integrating over the bearing length the estimated film pressure, the load carrying capacity or lifting force, frictional force and coefficient of friction are incorporated into the study.A comparison to a single porous layer underscores the superior efficiency offered by the double porous layer.The study further elucidates the most favourable inlet film height and the optimal bearing length that facilitate maximum load carrying capacity.It is evident that the inlet film thickness can be reached the maximum up to 1.8 to 2 to obtain the maximum load carrying capacity of the bearing.The present study is compared with the existent results and that is more efficient for the current bearing model.

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 score0.407
Threshold uncertainty score0.943

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 head0.214
Teacher spread0.197 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicTribology and Lubrication EngineeringFrench-language works237,207