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Record W2726140797 · doi:10.1063/1.4990088

Droplet impact onto a solid sphere: Effect of wettability and impact velocity

2017· article· en· W2726140797 on OpenAlexafffund
Sayed Abdolhossein Banitabaei, Alidad Amirfazli

Bibliographic record

VenuePhysics of Fluids · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingLamella (surface anatomy)Drop (telecommunication)Particle (ecology)PhysicsDrop impactCollisionSolid surfaceMechanicsRange (aeronautics)Critical ionization velocityParticle sizeSmoothed-particle hydrodynamicsComposite materialChemical physicsMaterials scienceThermodynamicsChemistryPhysical chemistryMechanical engineering

Abstract

fetched live from OpenAlex

Collision of a droplet onto a still spherical particle was experimentally investigated. The effect of droplet impact velocity and wettability of the particle surface on collision outcomes was studied (0.05 < V0 < 5.0 and θ = 70°, 90°, 118°). Compared to the literature, the range of Weber number variations was significantly extended (0.1 < We < 1146), and while focus of the previous works was on impacts in which particle is larger than the droplet (Dr < 1), the drop to particle diameter ratio in this work was larger than one. Therefore, formation of a thin liquid film, i.e., lamella, was observed due to impact of a relatively high velocity droplet onto a hydrophobic particle. Temporal variations of various geometrical parameters of collision outcomes including lamella length and lamella base diameter were investigated during the impact. It was also shown that for hydrophobic targets, the extent of hydrophobicity of the particle does not affect the lamella geometry. A comprehensive map of all the available works in drop impact on a spherical target was also provided.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.277
Teacher spread0.269 · 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

Citations143
Published2017
Admission routes2
Has abstractyes

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