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Record W1935835297 · doi:10.1002/cjce.22280

Hydromagnetic blasius flow of power‐law nanofluids over a convectively heated vertical plate

2015· article· en· W1935835297 on OpenAlexaffvenue
Waqar A. Khan, Richard Culham, Oluwole Daniel Makinde

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanofluidThermophoresisNusselt numberPrandtl numberMechanicsSherwood numberBoundary layerMaterials scienceThermodynamicsPhysicsHeat transferClassical mechanicsTurbulenceReynolds number

Abstract

fetched live from OpenAlex

In this study, the hydromagnetic Blasius flow of power‐law nanofluids is investigated numerically. A convectively heated impermeable vertical plate is used and a constant transverse magnetic field is applied at the plate surface. The characteristics of water‐based non‐Newtonian nanofluids are explored using a non‐Newtonian power‐law model. A Buongiorno model is employed to include the effects of Brownian motion and thermophoresis in the study. The governing mass, momentum, thermal energy, and nanoparticle concentration equations are transformed into nonlinear ordinary differential equations which are solved using a spectral relaxation method. The effects of nanofluid parameters ( ), power index , generalized Prandtl ( ), and Schmidt ( ) numbers on dimensionless velocity, temperature, concentration, skin friction, local Nusselt, and Sherwood numbers are explored. It is found that pseudoplastic nanofluids have higher skin friction and lower heat and mass transfer rates than dilatant nanofluids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations35
Published2015
Admission routes2
Has abstractyes

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