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Record W4406153113 · doi:10.1139/cjp-2024-0173

Thermodynamics feature of modified non-Newtonian fluid model over an exponentially curved stretching surface

2025· article· en· W4406153113 on OpenAlexaffvenue
Nadeem Abbas, Wasfı Shatanawi, Taqi A. M. Shatnawi

Bibliographic record

VenueCanadian Journal of Physics · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhysicsThermodynamicsNewtonian fluidSurface (topology)Exponential growthClassical mechanicsMechanicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

In this analysis, we have developed a new stress tensor of combined fluid models such as Sutterby fluid, Casson fluid, and micropolar fluid. The study focuses on fluid flow over a curved sheet stretched exponentially. Brownian motion and thermophoresis impacts are highlighted and considered to have a very low magnetic Reynolds number with heat generation effect. Based on certain flow assumptions, the mathematical model has been developed using boundary layer approximations in terms of coupled partial differential equations (PDEs). These PDEs have been transformed from ordinary differential equations and solved using numerical technique. The findings are presented through graphical and tabular data, illustrating the impacts of governing physical parameters on the system. Concentration curves show improving phenomena by enlarging values of the curvature factor. The Casson fluid parameter significantly impacts mass and heat transfer, resulting in higher rates while simultaneously reducing friction at the surface. The range of the physical factors is presented as 0.1 ≤ β ≤ 6.0, 0.0 ≤ Υ ≤ 5.0, 0.7 ≤ P r ≤ 10, 0.0 ≤ δ 1 ≤ 4.0, 0.0 ≤ Ec ≤ 5.0, 0.0 ≤ P ≤ 10.0, 0.0 ≤ M ≤ 5.0, 0.0 ≤ γ 1 ≤ 4.0, 0.0 ≤ N t ≤ 3, and 0.0 ≤ N b ≤ 3. The velocity becomes lesser due to increasing values of the Casson fluid factor and of the Sutterby fluid factor. The velocity revealed declining due to enrichment in the micropolar fluid parameter.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.762

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 head0.225
Teacher spread0.216 · 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 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

Citations4
Published2025
Admission routes2
Has abstractyes

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