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Record W2048451634 · doi:10.1063/1.1637351

Slow flow through a brush

2004· article· en· W2048451634 on OpenAlexaff
Mark F. Tachie, David F. James, I. G. Currie

Bibliographic record

VenuePhysics of Fluids · 2004
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsMechanicsCylinderParticle image velocimetryAnnulus (botany)BrushCouette flowShear flowFlow velocityVector fieldVelocimetrySlip (aerodynamics)Flow (mathematics)Classical mechanicsGeometryTurbulenceMaterials scienceComposite materialThermodynamics

Abstract

fetched live from OpenAlex

This paper reports velocity measurements of slow flow through a model brush. The flow field was created in the annulus between two concentric cylinders, which was filled with a viscous oil. The inner cylinder was stationary and the outer one was installed on a turntable and rotated at a constant speed. A brush was modeled by an array of uniformly spaced rods mounted horizontally onto the inner cylinder. With a generous gap between the rod ends and the outer cylinder, the flow outside the array was circular Couette flow. Three brushes were made and the external shear flow penetrated these because the solid volume fractions were small, namely, 0.025, 0.05, and 0.10. Particle image velocimetry was used to study the velocity field in the penetration region, and from these measurements the velocity at the interface, “the slip velocity,” was determined. The slip velocity was found to be close to the value predicted by Brinkman’s equation, and to be higher than velocities found previously for rod arrays in other configurations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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

Citations27
Published2004
Admission routes1
Has abstractyes

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