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Record W2023224669 · doi:10.1002/cjce.5450810502

Spray Characteristics of Two‐phase Feed Nozzles

2003· article· en· W2023224669 on OpenAlexafffundvenue
Siva Ariyapadi, Ram Balachandar, Franco Berruti

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of WindsorWestern University
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsNozzleJet (fluid)MechanicsEntrainment (biomusicology)TurbulenceSpray characteristicsMaterials scienceParticle (ecology)Phase (matter)Spray nozzlePhysicsThermodynamicsAcousticsGeology

Abstract

fetched live from OpenAlex

Abstract The present study focuses on understanding the spray characteristics of a turbulent gas‐liquid jet (Re liq = 24,000). Air and water are used as the test fluids. The angles of injection of the two phases upstream of the nozzle are varied (θ = 20°, 45° and 90°) and the effect of carrier gas on the droplet characteristics is are also investigated. The droplet size and velocity are non‐intrusively measured using a Phase‐Doppler Particle Analyzer (PDPA). In some respects, the characteristics of the present two‐phase jet are similar to those noticed in previous studies, while revealing some important differences. The centreline mean droplet velocities (15 ∼ 20 m/s) increase in the initial region of the jet, attain a maximum and then decrease at larger distances from the nozzle exit. Most of the entrainment occurs at the tip of the nozzle and the jet expansion rate decreases significantly at distances where the spray velocity profiles become self‐similar. A Lorentz‐type fit has been used to model the normalized radial velocity profiles. The results indicate that the test configuration with θ = 45° may be beneficial for the scenario discussed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.469

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations11
Published2003
Admission routes3
Has abstractyes

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