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GAS-PHASE PROBABILITY DISTRIBUTION IN LIQUID CROSS-FLOW

2014· article· en· W2253111063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMultiphase Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShadowgraphyMechanicsBreakupNozzleJet (fluid)Dimensionless quantityFlow (mathematics)Materials scienceTwo-phase flowTurbulenceReynolds numberPerpendicularThermodynamicsPhysicsOpticsMathematicsGeometry

Abstract

fetched live from OpenAlex

The interaction of an air jet in liquid water cross-flow in the vicinity of a gas injector was experimentally investigated using high-speed shadowgraphy. A turbulent, fully developed water flow, with superficial water velocity values of 1.9-4.3 m/s, was circulated through a 12.7-mm square channel of 118 cm in length. Three gas nozzles, with diameters of 0.27 mm, 0.52 mm, and 1.59 mm, were used to inject the air perpendicularly into the water flow. The gas mass flow rates ranged from 10-60 × 10-3 g/s. An image-processing algorithm was used to estimate the incipient centerline and borderline trajectories of the gas phase during its initial interaction with the liquid. Experimental results were compared with existing correlations developed for standard jets in a cross-flow and showed limited agreement. The lack of correlation specifically for gas jets in a cross-flowing liquid is substantial. For this reason, original empirical expressions based on dimensionless parameters were introduced. The assessment of the correlations indicated a dependable prediction of the initial centerline and borderline trajectories of the gas jet in liquid.

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.

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)0.001
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 score0.962
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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