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Record W2345416723 · doi:10.1002/cjce.22527

Behaviour and dynamics of two bubbles in conjunct condition in high‐viscosity liquids

2016· article· en· W2345416723 on OpenAlexvenueno aff
Junjie Feng, Xinchen Li, Yuyun Bao, Ziqi Cai, Zhengming Gao, Geoffrey M. Evans

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBubbleViscosityMechanicsDragWork (physics)Range (aeronautics)Materials scienceThermodynamicsPhysicsVolume (thermodynamics)Composite material

Abstract

fetched live from OpenAlex

Abstract Two bubbles in conjunct condition are often encountered in high‐viscosity liquids, but have received very little attention in the literature. The conjunct bubbles rise together with unchanged shapes and constant velocities, showing some unique properties compared to single bubbles. The current research built on the previous work of Cai et al.,[1] and extended the bubble size ratio (κ) to the range 1.0–1.2. The formation and motion characteristics of conjunct bubbles made by direct collision of two in‐line bubbles were investigated, including the effects of bubble volume, bubble size ratio, and liquid viscosity. Models for the conjunct bubbles and the single bubbles were provided for predicting the projected area diameters and the rising velocities respectively, and the predicted values closely agreed with the measurements in glycerol‐water solutions with Morton numbers in the range of 1.690–661.8. The dynamic forces on the conjunct bubbles were analyzed, and a succinct algorithm was proposed for calculating the drag forces on the conjunct bubbles and the interaction force between the two bubbles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.003
GPT teacher head0.175
Teacher spread0.172 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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