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Record W1486355877 · doi:10.4271/2000-01-2322

Transient Convection Flow in Super-Imposed Fluid and Porous Layers

2000· article· en· W1486355877 on OpenAlexfundno aff
M. Ziad Saghir, M. R. Islam, Jean Claude Legros

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransient (computer programming)MechanicsFluid dynamicsPorosityTransient flowConvectionFlow (mathematics)Transient analysisPorous mediumMaterials scienceGeologyComputer scienceTransient responsePhysicsComposite materialSurgeEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper is aimed at investigating the effects of combined Marangoni and Rayleigh convections in a porous layer, underlain by a fluid layer. The two-dimensional transient numerical model represents a dual rectangular cavity system in which the porous cavity is located below the fluid cavity. Both cavities are saturated with hexane. The problem consists of studying the combined Marangoni and Rayleigh effects on the flow. The interaction between the Marangoni and the Rayleigh convection is investigated in detail. The porous cavity is heated at the bottom while the top liquid cavity has a free surface and is maintained at a room temperature. In addition, the role of the aspect ratio of the porous layer over the fluid layer in determining the convection pattern was studied. Results indicate that the Marangoni convection enhances the flow in the porous layer for high aspect ratio while the Rayleigh number suppresses the Marangoni convection at the free surface for a low aspect ratio.. At a critical aspect ratio above 3, the Marangoni convection becomes dominant over the Rayleigh convection in the liquid layer.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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