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Record W2026539125 · doi:10.1002/cjce.20474

MHD stagnation point viscoelastic fluid flow and heat transfer on a thermal forming stretching sheet with viscous dissipation

2011· article· en· W2026539125 on OpenAlexvenueno aff
Kai-Long Hsiao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersNational Science Council
KeywordsHeat transferMechanicsPrandtl numberChurchill–Bernstein equationStagnation pointThermodynamicsDeborah numberBoundary layerConvective heat transferEckert numberMaterials scienceNewtonian fluidNusselt numberFlow (mathematics)PhysicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Abstract An incompressible steady two‐dimensional forced convection with magnetic hydrodynamic (MHD) second‐grade non‐Newtonian (Viscoelastic) fluid flow on a stagnation point of a thermal forming stretching sheet has been studied. A parameter M which is used to represent the dominance of the magnetic effect has been presented in governing equations. The similar transformation, the perturbation expansion and an implicit finite‐difference method have been used to analyse the present problem. The numerical solutions of the flow velocity distributions, temperature profiles, the wall unknown values of f ″(0) and θ '(0) for calculating the heat transfer of the similar boundary‐layer flow are carried out as functions of the viscoelastic number k , the Prandtl number Pr , the dissipation parameter E and the magnetic parameter M . The effects of these parameters have also discussed. The value of k is an important factor in this study. It will produce greater heat transfer effect with a larger k or Pr and parameters M or E will reduce heat transfer effect. The non‐Newtonian flow heat transfer effect is better than a Newtonian flow heat transfer effect.

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 score0.023
Threshold uncertainty score0.513

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 head0.166
Teacher spread0.158 · 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

Citations27
Published2011
Admission routes1
Has abstractyes

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