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Effects of Cattaneo-Christov Heat Flux on Casson Nanofluid Flow Past a Stretching Cylinder

2017· article· en· W2760525946 on OpenAlexaff
Oluwole Daniel Makinde, V. Nagendramma, C. S. K. Raju, A. Leelarathnam

Bibliographic record

VenueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsMontreal Police Service
Fundersnot available
KeywordsNanofluidThermophoresisHeat fluxMechanicsBoundary layerThermodynamicsPartial differential equationHeat transferCylinderNusselt numberSimilarity solutionMaterials sciencePhysicsMathematicsGeometryReynolds numberTurbulence

Abstract

fetched live from OpenAlex

In this paper, we investigate the combined effects of Brownian motion, thermophoresis and Cattaneo-Christov heat flux on Casson nanofluid boundary layer flow over a stretching cylinder. The governing partial differential equations (PDEs) are obtained and transformed into a system of ordinary differential equations (ODEs) by employing appropriate similarity solution. The model nonlinear boundary value problem is tackled numerically using fourth-fifth order Runge-Kutta integration scheme with shooting technique. Effects of various thermophysical parameters on the velocity, temperature and concentration profiles as well as skin friction and Sherwood number are presented graphically and discussed quantitatively. It is found that thermal relaxation parameter minimizes the temperature field and boosting the rate of heat transfer per unit volume. This heat flux conditions are very useful for thermal transport control in manufacturing and chemical industries.

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 score0.003

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)0.0010.000

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 head0.018
GPT teacher head0.262
Teacher spread0.245 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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Same venueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forumSame topicNanofluid Flow and Heat TransferFrench-language works237,207