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Record W2161664863 · doi:10.1002/aic.14440

Spread and recoiling of liquid droplets impacting solid surfaces

2014· article· en· W2161664863 on OpenAlexafffund
Xuan Gao, Ri Li

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingMechanicsDissipationRange (aeronautics)Solid surfaceFlow (mathematics)Energy (signal processing)Materials scienceSurface energyEnergy conservationContact angleSurface (topology)ThermodynamicsChemistryPhysicsGeometryComposite materialEngineeringChemical physicsMathematics

Abstract

fetched live from OpenAlex

The impact of water droplets on solid surfaces is studied experimentally and theoretically. Theoretical equations based on energy conservation are developed. In our theoretical study, the droplet is modeled as a ring‐like shape, which matches the dynamic shape of droplets observed from our experimental tests. In the analysis of energy conservation, the nonuniform distribution of pressure inside the deformed droplet is taken into account by introducing a flow potential energy term in the theoretical equations. To derive viscous dissipation for recoiling, a viscous layer coefficient is introduced. Its values for the tests using smooth surfaces are found to be within a small range. Both theoretical predictions and experimental data show significant influence of surface wettability on maximum spread and recoiling process. With the increase of advancing contact angle, surface energy shows a decreasing trend, whereas flow potential energy shows an increasing trend and becomes significant for hydrophobic surfaces. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2683–2691, 2014

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations41
Published2014
Admission routes2
Has abstractyes

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