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Record W2522938977 · doi:10.1139/cjp-2015-0508

Impact of curvature on the mixed convective peristaltic flow of shear thinning fluid with nanoparticles

2016· article· en· W2522938977 on OpenAlexvenueno aff
Iqra Shahzadi, S. Nadeem

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMechanicsNanofluidReynolds numberShear thinningCurvatureThermophoresisTangentBrownian motionConvectionHomotopy analysis methodFluid dynamicsClassical mechanicsShear flowGeometryRheologyHeat transferThermodynamicsTurbulenceMathematics

Abstract

fetched live from OpenAlex

The aim of the present analysis is to discuss the mixed convective peristaltic flow of shear thinning hyperbolic tangent fluid under the effects of nanoparticles in a curved channel. The model considered for the nanofluid is to analyze the effects of Brownian motion and thermophoresis parameter. The problem is formulated under the assumptions of long wavelength and low Reynolds number and then solved analytically using the homotopy perturbation method (HPM). The substantial features of related parameters are examined by sketching graphs. The most important observation of the analysis is that the velocity profiles are not symmetric about the central line of the curved channel.

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 score0.046
Threshold uncertainty score0.229

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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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