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Record W1989419124 · doi:10.1115/fedsm2012-72088

Mono-Disperse Spray Generation by a Flow Focusing Atomizer: A Numerical Study

2012· article· en· W1989419124 on OpenAlexaff
B. Samareh, Arash Kashani, J. Mostaghimi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVolume of fluid methodBreakupBody orificeMechanicsDrop (telecommunication)Flow (mathematics)TurbulenceBreak-UpPressure dropVolumetric flow rateFlow focusingMaterials scienceJet (fluid)InjectorBackflowThermodynamicsPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Pneumatic flow focusing technology is a relatively new atomizing process first introduced in 1998. A liquid microjet is formed by a high speed co-flowing gas stream when a pressure drop is applied across an orifice. The microjet eventually disintegrates into fine droplets by the perturbing gas downstream of the atomizer exit. Under certain controlled flow regimes the resulting spray demonstrates strong monodispersity. In the present study, the gas flow is numerically modeled by a compressible turbulent flow solver and the liquid jet evolution is captured using Volume of Fluid (VOF) interface tracking method. Jet breakup parameters and drop size distribution are studied for different liquids and flow rates. Results are in good agreement with experiments and theoretical models.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.455

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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
Published2012
Admission routes1
Has abstractyes

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