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Record W4229040203 · doi:10.1063/5.0091699

A three-dimensional numerical model for the motion of liquid drops by the particle finite element method

2022· article· en· W4229040203 on OpenAlexafffund
Elaf Mahrous, R. V. Roy, Alex Jarauta, Marc Secanell

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsAlberta EnergyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Commission for Jubail and Yanbu
KeywordsPhysicsMechanicsFinite element methodDeformation (meteorology)Drop (telecommunication)DissipationBoundary value problemNumerical analysisParticle (ecology)Classical mechanicsParticle-laden flowsStatistical physicsTwo-phase flowMathematical analysisMechanical engineeringThermodynamicsFlow (mathematics)Meteorology

Abstract

fetched live from OpenAlex

Analysis of drop spreading and sliding on solid substrates is critical for many industrial applications, such as microfluidic devices, cooling towers, and fuel cells. A new three-dimensional model is proposed for droplet dynamics. Its numerical solution is obtained by the particle finite element method, based on an updated Lagrangian framework to accurately track the deformation of the droplet. The model hinges on boundary conditions at the solid–liquid interface to account for viscous dissipation and retention forces. These conditions are essential to obtain mesh-independent solutions and a realistic spatiotemporal evolution of the droplet deformation. Several numerical simulations are performed to assess the performance of the model for spreading and sliding drops, and results are compared to experimental data found in the literature. Good agreement is obtained with the available data. Simulations performed in two dimensions show striking discrepancies with the experimental data, thus demonstrating the need for three-dimensional simulations.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.552
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.295
Teacher spread0.249 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

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