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Record W3154502594 · doi:10.1063/5.0047279

Two-layer modeling of thermally induced Bénard convection in thin liquid films: Volume of fluid approach vs thin-film model

2021· article· en· W3154502594 on OpenAlexafffund
Ali Mohammadtabar, Hadi Nazaripoor, Adham Riad, Arman Hemmati, Mohtada Sadrzadeh

Bibliographic record

VenueAIP Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVolume of fluid methodMarangoni effectInstabilityMechanicsMaterials scienceConvectionWavelengthDeformation (meteorology)Thin filmVolume (thermodynamics)Marangoni numberThermodynamicsComposite materialPhysicsNanotechnologyBreakup

Abstract

fetched live from OpenAlex

This study focuses on a detailed analysis of thermally induced Bénard convection, thermocapillary instability, and interfacial deformation of a nanofilm. The dynamics, instability, and morphological evolution of a thin liquid film investigated using a volume of fluid (VOF) numerical scheme that incorporates the Marangoni stress to model the gas–liquid interface deformation. The results obtained from VOF are then compared with those of the “thin-film” model in many cases to find an accurate model for predicting the characteristic wavelength for the growth of instabilities. We also present a correlation to predict the relation between the characteristic wavelength found by VOF numerical results and the analytical linear stability analysis predictions. This is followed by examining the protrusion width and the distance between the protrusions on the structures’ final shape and interface evolution time. Finally, linear theoretical relations for the formation of secondary pillars are presented based on the width of protrusions, their separation distance, and the inverse filling ratio. The results show that the number of pillars increases when the width and distance between two protrusions are greater than a critical value.

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 score0.124
Threshold uncertainty score0.862

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 head0.230
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 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

Citations1
Published2021
Admission routes2
Has abstractyes

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