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Electrohydrodynamic Instability in a Heat Generating Porous Layer Saturated by a Dielectric Nanofluid

2017· article· en· W2741684353 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsAthabasca University
Fundersnot available
KeywordsThermophoresisNanofluidLewis numberRayleigh numberElectrohydrodynamicsThermodynamicsMechanicsHeat fluxConvectionMaterials sciencePorous mediumDarcy numberThermal diffusivityNatural convectionHeat transferElectric fieldPhysicsPorosityComposite material

Abstract

fetched live from OpenAlex

In this paper, a theoretical investigation has been carried out to study the combined effect of AC electric field and temperature depended internal heat source on the onset of convection in a porous medium layer saturated by a dielectric nanofluid. The model used for nanofluid incorporates the combined effect of Brownian diffusion, thermophoresis and electrophoresis, while for porous medium Darcy model is employed. The flux of volume fraction of a nanoparticle with the effect of thermophoresis is taken to be zero on the boundaries and the eigenvalue problem is solved using the Galerkin method. Principle of exchange of stabilities is found to be valid and subcritical instability is ruled out. The results show that increase in the internal heat source parameter , AC electric Rayleigh-Darcy number , the Lewis number , the modified diffusivity ratio and the concentration Rayleigh-Darcy number are to hasten the onset of convection. The size of convection cells decreases with increasing the internal heat source parameter and the AC electric Rayleigh-Darcy number .

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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