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Record W2027294712 · doi:10.1615/atomizspr.v17.i3.30

ROLE OF VISCOSITY ON TRAJECTORY OF LIQUID JETS IN A CROSS-AIRFLOW

2007· article· en· W2027294712 on OpenAlexaff
Madjid Birouk, C. O. Iyogun, N. Popplewell

Bibliographic record

VenueAtomization and Sprays · 2007
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNozzleViscosityMechanicsPenetration (warfare)Materials scienceJet (fluid)Penetration depthAirflowWind tunnelThermodynamicsPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

The effect of liquid viscosity on the penetration and trajectory of a jet in a low subsonic cross-airflow was investigated experimentally. An open-loop wind tunnel was used to generate an airstream in a square cross-sectional test section. Liquid was injected downward through a nozzle that was flush with the top inner surface of the test section. A wide range of experimental conditions was achieved by varying the nozzle diameter, momentum flux ratio, and liquid viscosity. The study revealed that viscosity has distinct effects on the initial part of the liquid column and in the jet's far-field stream. It was shown that far from the nozzle exit, the jet's penetration increased initially as the liquid viscosity increased, but a further increase in viscosity reduced the penetration. On the other hand, close to the nozzle exit, although the effect of liquid viscosity was not obvious, it was generally observed that with the exception of the highest viscosity employed here, the jet's penetration decreased as the viscosity increased. An empirical jet trajectory correlation was proposed to account for the combined effects of viscosity, momentum flux ratio, and nozzle diameter.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.239
Teacher spread0.232 · 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 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

Citations44
Published2007
Admission routes1
Has abstractyes

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