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Record W4214760321 · doi:10.1002/num.22874

<scp>Cattaneo–Christov</scp> heat flux model for three‐dimensional magnetohydrodynamic flow of an Eyring Powell fluid over an exponentially stretching surface with convective boundary condition

2022· article· en· W4214760321 on OpenAlexaff
T. A. Yusuf, M. Bilal Ashraf, Fazle Mabood

Bibliographic record

VenueNumerical Methods for Partial Differential Equations · 2022
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
Fundersnot available
KeywordsMagnetohydrodynamic driveMechanicsHeat fluxConvectionHeat transferCompressibilityNanofluidConvective heat transferThermodynamicsParasitic dragHeat transfer coefficientFlow (mathematics)MagnetohydrodynamicsBoundary layerMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

Abstract The present model concentrates on three‐dimensional steady incompressible flow of an Eyring‐Powell nanofluid past an exponentially stretching sheet with magnetic field. The Cattaneo–Christov heat flux with convective boundary condition is accounted for. Shooting method is the instrumental for obtaining numerical solution of the transformed‐converted system of the mathematical models. Behavior of the determining thermo‐physical parameters on the velocity, temperature, skin friction, heat transfer rate, and finally isotherms are considered. The major relevant outcomes of the current investigation are that increment in Eyring‐Powell parameter uplifts flow velocity, while that peters out the fluid temperature. Enhanced values of the mixed convection parameter weakened the skin friction coefficient while it slightly strengthened the rate of heat transfer.

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)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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