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Record W2140159339 · doi:10.1139/p05-056

Non-Darcy Couette flow through a porous medium of magnetohydro-dynamic visco-elastic fluid with heat and mass transfer

2005· article· en· W2140159339 on OpenAlexvenueno aff
Nabil T. Eldabe, S. Sallam

Bibliographic record

VenueCanadian Journal of Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsNusselt numberMechanicsPorous mediumDarcy numberMass transferHeat transferFluid dynamicsMagnetohydrodynamic driveCouette flowCompressibilitySherwood numberChurchill–Bernstein equationThermodynamicsClassical mechanicsFlow (mathematics)MagnetohydrodynamicsPorosityMagnetic fieldReynolds numberMaterials scienceTurbulence

Abstract

fetched live from OpenAlex

We analyze the steady magnetohydrodynamic flow of an incompressible electrically conducting visco-elastic fluid through a porous medium between two porous parallel plates under the influence of a transverse magnetic field. We obtain an exact solution for the Brinkman–Forchheimer extension of Darcy's momentum equation for flow. We solve the equations of motion with a perturbation technique under the assumption that the Forchheimer number Fs = bν/u0 is small. We analyze heat and mass transfer in porous media. We obtain the skin friction τw, the Nusselt number Nu, and the Sherwood number Sh. Our numerical results show the effects of the physical parameters of our problem on the fluid flow as well as on the heat and mass transfer, on the skin friction, and on the rates of heat and mass transfer. PACS No.: 47.65.+a

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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

Citations18
Published2005
Admission routes1
Has abstractyes

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