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Record W4386461379 · doi:10.1063/5.0165682

Numerical study of droplet impingement and spreading on a moving surface

2023· article· en· W4386461379 on OpenAlexaff
Ningli Chen, Alidad Amirfazli

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsLamella (surface anatomy)MechanicsPhysicsDrop (telecommunication)Capillary actionShear stressRelative velocityShear flowRelative motionClassical mechanicsMaterials scienceComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The formation of an asymmetric lamella when a drop impacts a moving surface has been observed, but the underlying mechanism is not fully understood. In this study, we implemented a coupled level-set and volume-of-fluid method to simulate the asymmetric spreading of a drop on a moving surface. The dynamic contact angle model was used, with the capillary number calculated from the relative velocity at the contact line. The numerical method was validated with experimental data from the literature, and the spreading dynamics were also analyzed. The results indicate that the current method yields accurate predictions for lamella spreading, with a relative error in lamella width of less than 5%. This study reveals that the moving surface affects the spreading through the shear stress transferred from the surface to the liquid and the translation motion of the surface. Shear stress causes the lamella to either stretch or squeeze and the translation motion of the surface results in the advancing and receding phases existing together. These mechanisms lead to asymmetric spreading, and the asymmetricity of the lamella increases with the surface velocity and liquid viscosity. When the surface velocity is small, the effect of shear stress and translational motion only causes a translation of the lamella with no asymmetric spreading.

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 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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 teacher head, 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

Citations3
Published2023
Admission routes1
Has abstractyes

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