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NATURAL CONVECTION IN A HORIZONTAL ANNULAR POROUS CAVITY SATURATED BY A BINARY MIXTURE

2011· article· en· W2001213841 on OpenAlexaff
Zineddine Alloui, P. Vasseur

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNatural convectionBuoyancyLewis numberRayleigh numberMechanicsMaterials scienceConvectionPorosityThermodynamicsThermophoresisEnclosureCombined forced and natural convectionRADIUSPorous mediumHeat transferPhysicsNanofluidMass transferComposite material

Abstract

fetched live from OpenAlex

The Darcy model with the Boussinesq approximation is used to study natural convection in a horizontal annular porous layer filled with a binary fluid under the influence of the Soret effect. Both the inner and the outer cylinders are kept at constant temperature with the inner surface higher than that of the outer. The governing parameters for the problem are the Rayleigh number Ra, the Lewis number Le, the buoyancy ratio φ, the radius ratio of the cavity R, and the normalized porosity ε. Two main convective modes are studied, namely, single- and double-cell convection in each half of the enclosure. Numerical solutions of the full governing equations are obtained for a wide range of the governing parameters. The existence of dual solutions for a range of the buoyancy ratio φ that depends on the other governing parameters has been demonstrated. Also, oscillating flows are obtained on increasing or decreasing φ beyond critical values.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.244
Teacher spread0.228 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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Same venueComputational Thermal Sciences An International JournalSame topicNanofluid Flow and Heat TransferFrench-language works237,207