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Record W4406525799 · doi:10.1016/j.jcis.2025.01.096

Viscous fingering and interface splitting instabilities in air–water-oil systems

2025· article· en· W4406525799 on OpenAlexafffund
Younghoon Lee, Jingyi Wang, Ranjani Kannaiyan, Ian D. Gates

Bibliographic record

VenueJournal of Colloid and Interface Science · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence FundAlberta Innovates
KeywordsViscous fingeringAir waterMechanicsInterface (matter)Materials sciencePetroleum engineeringEnvironmental scienceChemistryGeologyPorous mediumComposite materialPhysicsBubble

Abstract

fetched live from OpenAlex

HYPOTHESIS: Viscous fingering instabilities of air displacing water displacing mineral oil is controlled by the air injection rate. Given the lower viscosity of the water, air would tend to finger through the water and then after it reaches the oil, proceed to finger through the oil. EXPERIMENTS: In a radial Hele-Shaw cell, experiments were conducted on air injection into mineral oil and air injection into a volume of water at the center of the cell which in turn is surrounded by mineral oil. The images of the fingers are analyzed to quantify the fingering patterns to compare the behaviours between the two and three phase systems. FINDINGS: Significantly different fingering patterns occur between the two and three phase systems. When air fingers penetrate the water layer and reach the oil, the air propagates along the interface between water and oil in an interface splitting instability mode resulting in development of more intricate fingering patterns. It was also observed that air fingers penetrate within water fingers that penetrate the oil. In addition, the fingertip moves faster with the presence of water exhibiting a discontinuity of the fingertip velocities across the water and mineral oil regions.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.219
Teacher spread0.215 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes2
Has abstractyes

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