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Record W2089773259 · doi:10.1139/p09-047

Viscous dissipation effect on mixed convection flow of a micropolar fluid over an exponentially stretching sheet

2009· article· en· W2089773259 on OpenAlexvenueno aff
Mohamed Abd El-Aziz

Bibliographic record

VenueCanadian Journal of Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPrandtl numberNusselt numberMechanicsBoundary layerPhysicsCombined forced and natural convectionFilm temperatureEckert numberParasitic dragBuoyancyThermodynamicsFlow (mathematics)Heat transferConvectionNatural convectionReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Micropolar boundary-layer flow and heat transfer characteristics associated with a heated exponential stretching continuous sheet being cooled by a mixed convection flow are examined. The relevant heat transfer mechanisms are of interest in a wide variety of practical applications such as hot rolling, continuous casting, extrusion, and drawing. The wall temperature and stretching velocity are assumed to vary according to specific exponential forms. The contributions of buoyancy along with viscous dissipation on the convective transport in the boundary-layer region is analyzed in the opposing and assisting flow situations. Local similarity solutions are obtained for the boundary-layer equations governing the problem. A parametric study of the mixed convection parameter ξ, the micropolar parameter Δ, the Eckert number Ec, the parameter of temperature distribution n, and Prandtl number Pr is conducted and a representative set of numerical results for the velocity, angular velocity, temperature profiles, local skin friction coefficient, wall couple stress parameter, and local Nusselt number are illustrated graphically to show typical trends of the solutions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations183
Published2009
Admission routes1
Has abstractyes

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