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Record W2908317321 · doi:10.1139/cjp-2018-0526

Stability analysis of Cu–H<sub>2</sub>O nanofluid over a curved stretching–shrinking sheet: existence of dual solutions

2019· article· en· W2908317321 on OpenAlexvenueno aff
Usama Usama, S. Nadeem, Arif Ullah Khan

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidPhysicsCurvatureMechanicsNusselt numberThermodynamicsReynolds numberGeometryHeat transferMathematics

Abstract

fetched live from OpenAlex

The effect of mass suction with temperature jump and velocity slip of viscous, unsteady nanofluid flow past a curved shrinking–stretching surface is analyzed in this work. Copper (Cu) and water are considered nanoparticles and base fluids, respectively. The complicated coupled system of differential equations is converted into non-dimensional form with some suitable similarity variables. The solution of the nonlinear problem is produced by use of numerical scheme available in the form of bvp4c package in MATLAB. In the case of shrinking towards the surface, a reverse flow situation is also developed and requires careful selection of solution by examining the stability of the solution. Detailed stability analysis is done and critical values are determined for the possible existence of dual solutions. Variation in parameters is analyzed by plotting graphs and tables. The numerical values are also calculated for the reduced Nusselt number and skin friction due to variation in values of different flow parameters. Results have shown that for the curved shrinking surfaces, one should expect multiple solutions for a set of parameter values such as mass suction, curvature, nanoparticles volume fraction, and unsteadiness.

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 score0.107
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.201
Teacher spread0.183 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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