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Record W2012007995 · doi:10.1002/aic.13737

Elastic liquid jet impaction on a high‐speed moving surface

2012· article· en· W2012007995 on OpenAlexafffund
Bavand Keshavarz, Sheldon Green, Donald T. Eadie

Bibliographic record

VenueAIChE Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSplashDeborah numberReynolds numberMechanicsWeber numberNewtonian fluidElasticity (physics)Jet (fluid)Non-Newtonian fluidMaterials sciencePhysicsComposite materialThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

Abstract In the railroad industry a friction‐modifying non‐Newtonian liquid, showing elastic behavior, may be applied to the rail in the form of a liquid jet. The interaction of this elastic liquid jet and the moving surface—specifically whether it splashes or adheres without splash—is important in this industrial application. Twelve different elastic liquids with widely varying relaxation times were tested to isolate the effect of elasticity from other fluid properties. Using high‐speed imaging, the interaction between the impinging jet and the moving surface could be captured and analyzed. Although similar to Newtonian jets, for which the Reynolds number plays a major role, the Deborah number was also salient to the splash of elastic liquids. At the elevated Weber numbers of the testing, the Weber number had a much smaller impact on splash than did the Reynolds or Deborah numbers. The ratio of the surface velocity to the jet velocity has only a small effect on the splash. © 2012 American Institute of Chemical Engineers AIChE J, 2012

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations16
Published2012
Admission routes2
Has abstractyes

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