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Record W2001630002 · doi:10.1002/cjce.20701

Hydrodynamics of a rectangular liquid JET in an immiscible liquid–liquid system

2011· article· en· W2001630002 on OpenAlexvenueaboutno aff
Tejas Bhatelia, Harisinh Parmar, Chi M. Phan, Vishnu Pareek, Moses O. Tadé, Geoffrey M. Evans

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleBreakupMechanicsJet (fluid)Materials scienceVolume of fluid methodComputational fluid dynamicsVolumetric flow rateWeber numberAspect ratio (aeronautics)Reynolds numberNeckingThermodynamicsComposite materialPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract Volume of fluid and continuum surface force methodologies were applied to two‐ and three‐dimensionally model the motion of a liquid jet injected vertically downward from a rectangular nozzle into another immiscible liquid. Grid independent solutions were obtained for a 10 mm 2 nozzle with aspect ratios in the range 1–10. It was found that unlike the 3D simulation, the 2D CFD model was not able to predict the necking and breakup features observed in the experimental system. The 3D model showed that upon exiting the rectangular nozzle the liquid jet underwent a transition before becoming circular in cross‐section and eventually reaching an equilibrium diameter prior to breakup into droplets. For a given nozzle geometry it was found that equilibrium jet diameter increased with increasing liquid volumetric flowrate, with good agreement between CFD simulations and experimental observations. The 3D model was applied to rectangular nozzles with different aspect ratios and it was found that for a given liquid flowrate there was an optimum aspect ratio for generating minimum‐sized droplets, which was approximately 30% less than for a circular nozzle with the same cross‐sectional area. © 2011 Canadian Society for Chemical Engineering

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.682

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 head0.170
Teacher spread0.161 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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