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Record W4408626725 · doi:10.1002/cjce.25680

Study on the distribution of critical liquid bridge length in droplet formation under dripping regime in an annular shear flow field

2025· article· en· W4408626725 on OpenAlexvenueno aff
Yannan Sun, Mingyang Zhang, Jie Cheng, Shuaiyin Ma, Wenjie Zhu, Yaojun Guo, Jinhui Zhu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsMechanicsShear flowShear (geology)Field (mathematics)Materials scienceFlow (mathematics)Distribution (mathematics)PhysicsMathematicsComposite materialMathematical analysis

Abstract

fetched live from OpenAlex

Abstract A simplified experimental device was used to simulate the annular shear flow field in a liquid–liquid cyclone reactor, and the formation and break up behaviour of the liquid bridge during the droplet formation under the dripping regime was investigated. Deionized water and fluorescent oil were used as continuous phase and dispersed phase, respectively. The formation and break up process of dispersed phase liquid bridge in the annular simplified experimental device in the dripping regime was captured by camera. The impacts of the ratio of continuous phase velocity to dispersed phase velocity (Vr) and the capillary number (Ca) on the mean critical liquid bridge length (MBL) were analyzed. Furthermore, the functional relationships between MBL and operating parameters (Vr and Ca) under the dripping regime were obtained. The results indicate that the process of droplet formation is mainly affected by viscous force, shear force and surface tension. MBL increases firstly and then decreases with the increase of the two‐phase velocity ratio in dripping regime. Additionally, with the increase of capillary number, MBL decreases.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.307

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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