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Investigation and visualization of liquid–liquid flow in a vertically mounted Hele-Shaw cell: flow regimes, velocity and shape of droplets

2009· article· en· W2004557727 on OpenAlexaff
Saeed Shad, Ian D. Gates, Brij Maini

Bibliographic record

VenueMeasurement Science and Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCapillary actionHele-Shaw flowMechanicsCapillary numberDimensionless quantityMaterials scienceTwo-phase flowFlow (mathematics)Drop (telecommunication)Liquid dropViscous liquidFlow visualizationViscous fingeringFlow velocityOpen-channel flowPhysicsPorous mediumComposite materialPorosity

Abstract

fetched live from OpenAlex

The motion and shape of a liquid drop flowing within a continuous, conveying liquid phase in a vertical Hele-Shaw cell were investigated experimentally. The continuous phase was more viscous and wetted the bounding walls of the Hele-Shaw cell. The gap between the Hele-Shaw plates was set equal to 0.0226 cm. Four different flow regimes were observed: (a) small-droplet flow, (b) elongated-droplet flow, (c) churn flow and (d) channel flow. At low capillary number, that is, when capillary forces are larger than viscous forces, the droplet shape was irregular and changed with time and distance, and it moved with lower velocity than that of the conveying phase. At higher capillary number, several different shapes of stabilized elongated and flattened drops were observed. In contrast to gas–liquid systems, the velocities of droplets are higher than that of conveying liquid. New correlations derived from dimensionless analysis and fitted to the experimental data were generated to predict the elongated-drop velocity and aspect ratio.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.213
Teacher spread0.201 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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