MétaCan
Menu
Back to cohort
Record W2761309904 · doi:10.1002/cjce.23040

Visualization study on coalescence of droplets with different sizes in external liquid

2017· article· en· W2761309904 on OpenAlexvenueno aff
Chaoqun Shen, Xiangdong Liu, Cheng Yu, Yongping Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCapillary actionCoalescence (physics)MechanicsCapillary waveInertiaCapillary numberWeber numberMaterials scienceSurface tensionPhysicsClassical mechanicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

We experimentally investigate the coalescence between two droplets with different sizes in the surrounding water via high‐speed visualization. We identify three coalescence patterns by clarifying the dynamic interface evolutions, including liquid bridge evolution, capillary wave propagation, and pinch‐off behaviours. The results indicate that the coalescence patterns are directly related to the propagation of capillary waves on the coalescent droplet, which is governed by the competition among the capillary force, viscous force, and inertia involved in the draining from the original droplets into the liquid bridge. The external water can efficiently damp the oscillation in capillary wave propagation after the coalescence. In the inertial regime after the droplet coalescence, the evolution of liquid bridge is observed to follow a linear scaling law, when the capillary force induced by the azimuthal interface of the liquid bridge drives the liquid bridge expansion. Accordingly, a phase diagram is organized to characterize these coalescence patterns depending on Ohnesorge number, relative viscosity between external water and droplet, and size ratio between two coalesced droplets.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.293

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and Heat TransferFrench-language works237,207