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Droplet Splashing on an Inclined Surface

2019· article· en· W2914013773 on OpenAlexaff
Jiguang Hao, Lu Jie, Liaonan Lee, Zhihu Wu, Gengkai Hu, J. M. Floryan

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsSplashMechanicsMaterials scienceInclination angleLamella (surface anatomy)Oblique caseOpticsPhysicsMeteorologyGeometryComposite material

Abstract

fetched live from OpenAlex

Oblique droplet impacts onto a smooth surface at various inclination angles and at different ambient gas pressures were investigated using high-speed photography. It was found that the droplet splash can be entirely suppressed either by increasing the inclination angle or by reducing the ambient pressure. Variations of the threshold angle required for the splash suppression as a function of the impact velocity were determined, as well as the threshold pressure as a function of the inclination angle and the impact velocity. The threshold pressure increases monotonically as the inclination angle increases for small enough impact velocities but varies in a nonmonotonic manner for high enough impact velocities. Modifications of the existing splash model permit the theoretical determination of the splash threshold conditions that agree well with the experimental observations. It is shown that it is the velocity of the lamella tip that determines the splash onset.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.249
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations98
Published2019
Admission routes1
Has abstractyes

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