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Record W2133268514 · doi:10.5897/jmer.9000006

An integral treatment for combined heat and mass transfer by mixed convection along vertical surface in a saturated porous medium

2011· article· en· W2133268514 on OpenAlexvenueno aff
V. J. Bansod, Babasaheb Ambedkar

Bibliographic record

VenueMechanical Engineering Research · 2011
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNusselt numberCombined forced and natural convectionMass transferThermodynamicsHeat transferLewis numberPorous mediumMechanicsSherwood numberBoundary layerSimilarity solutionNatural convectionFilm temperatureMaterials scienceBuoyancyForced convectionConvective heat transferConvectionPorosityPhysicsComposite materialReynolds number

Abstract

fetched live from OpenAlex

Combined free and forced convection heat and mass transfer over an impermeable vertical surface embedded in a saturated porous medium is considered. A similarity transformation has been used to study the mixed convection boundary-layer flow over a semi-infinite vertical flat plate. Integral solutions are derived for the coupled nonlinear similarity equations of coupled heat and mass transfer in porous media for the case where the free stream velocity and temperature and concentration near the wall are kept constant. The governing parameters for the problem under consideration are the Lewis number Le, the buoyancy ratio N and mixed convection parameter (). The results for the heat and mass transfer coefficients in terms of Nusselt and Sherwood numbers are represented graphically for the various values of governing parameters of the problem. The heat and mass transfer in the boundary layer region has been analyzed for aiding and opposing flows.   Key words: Convection in porous media, heat and mass transfer, integral method, boundary 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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.042
GPT teacher head0.275
Teacher spread0.233 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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