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Record W2567531246 · doi:10.1615/atomizspr.2016013860

EFFECT OF ATOMIZER INTERNAL GEOMETRY ON THE INTERNAL AND EXTERNAL TWO-PHASE FLOW IN EFFERVESCENT ATOMIZATION

2016· article· en· W2567531246 on OpenAlexafffund
Mona Hassanzadeh Jobehdar, Kamran Siddiqui, Aly H. Gadallah, Wajid A. Chishty

Bibliographic record

VenueAtomization and Sprays · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsNational Research Council CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixing (physics)Materials scienceConical surfaceMechanicsBubbleFlow (mathematics)Tube (container)Base (topology)AerationInternal flowPhase (matter)Trailing edgeComposite materialChemistryPhysicsMathematics

Abstract

fetched live from OpenAlex

An experimental study was conducted to study the internal and external two-phase flow in an effervescent atomizer using high-speed imaging. The impact of the aerator tube configuration and mixing zone length on the size of bubbles inside the mixing zone and the spray droplet characteristics at different gas-to-liquid flow rates ratios were studied. An aerator tube with the conical end base was manufactured and tested. It is observed that the separation bubble at the trailing edge is suppressed by this configuration, which resulted in more uniform and smaller bubbles compared to the standard aerator tube with a flat base. The length of the mixing zone was found to have an impact on the bubble size distribution inside the mixing zone; more uniform and smaller bubbles are generated in the shorter mixing zone. The mixing zone length, however, does not show a distinct impact on the droplet velocity and size. It is concluded that a conical base aerator tube and a shorter mixing zone could significantly improve the spray steadiness and the atomization process.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.227
Teacher spread0.223 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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