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Record W1120742613 · doi:10.1615/atomizspr.2015011881

A MODEL FOR PREDICTING THE TRAJECTORY OF A LIQUID JET IN A SUBSONIC GASEOUS CROSSFLOW

2015· article· en· W1120742613 on OpenAlexafffund
Mohsen Broumand, Madjid Birouk

Bibliographic record

VenueAtomization and Sprays · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsJet (fluid)Reynolds numberTrajectoryDragPhysicsSurface tensionClassical mechanicsThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

This paper presents an approach for modeling a liquid jet trajectory in a subsonic gaseous crossflow. Forces acting on the liquid column including drag, gravitation, surface tension, and viscosity are all accounted for along with the mass and energy conservation equations which are employed to model the liquid jet trajectory. The tangential and normal components of the governing equations are solved analytically using control-volume analysis. A novel correlation in a sinusoidal-exponential functional form is developed as a function of the momentum flux ratio, gas and jet Weber number, jet Reynolds number, and Bond number. This correlation is capable of predicting jet trajectory of different liquids in a subsonic crossflow at different operating conditions and injection angles. The predictions showed reasonable agreement with published experimental data and empirical correlations.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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