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Record W2124380477 · doi:10.1002/fld.1076

Three-dimensional lubrication flow of a Herschel-Bulkley fluid

2005· article· en· W2124380477 on OpenAlexaff
Jing Zhang, Roger E. Khayat, Alphonso P. Noronha

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsLubricationMechanicsHerschel–Bulkley fluidReynolds equationFlow (mathematics)MathematicsRheologyPower-law fluidMaterials scienceGeometryReynolds numberPhysicsComposite material

Abstract

fetched live from OpenAlex

In this paper three-dimensional lubrication flow of grease is analysed numerically. The lubrication flow configuration is formed by two ellipsoid rollers. The load is assumed to be light enough for the lubrication mode to be purely hydrodynamic. The fluid behaviour is modelled using the Herschel–Bulkley model, and a two-dimensional modified Reynolds equation is derived. The numerical solutions are obtained by using a hybrid spectral/iterative technique and the Galerkin projection scheme. The effects of the material and geometrical parameters on pressure distribution are emphasized in the study. The investigation is conducted for a situation where the two ellipsoids are fully immersed in a grease lubricant. The effect of the geometry on the pressure distribution is determined by varying the ratio of the semi-axes and the minimum gap of the two rollers, respectively. The effect of the material parameters is examined by varying the power-law index and yield stress. It is found that the pressure distribution is strongly influenced by the shape of the rollers, the size of the minimum gap of the rollers and the rheological parameters. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.028
GPT teacher head0.371
Teacher spread0.343 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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