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Record W7117309651 · doi:10.5206/mase/22909

Influence of slip boundary conditions on the flow characteristics of sutterby mhd fluids over stretching surfaces

2025· article· en· W7117309651 on OpenAlexvenueno aff
Lateef Sogbetun, Bakai Ishola Olajuwon, O O Fagbemiro

Bibliographic record

VenueMathematics in Applied Sciences and Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetohydrodynamic drivePrandtl numberSlip (aerodynamics)Boundary value problemThermal radiationNonlinear systemThermalShooting methodPartial differential equationMagnetohydrodynamics

Abstract

fetched live from OpenAlex

The application of multiple slip mechanisms in non-Newtonian fluid transport arises in various engineering processes, including lubrication, polymer extrusion, surface coating, and drag reduction in pipelines. This study investigates the effects of velocity and thermal slip on the flow and thermophysical behaviour of a magnetohydrodynamic (MHD) Sutterby fluid over a stretching surface. The mathematical model incorporates heat generation, nonlinear radiative heat transfer, thermal diffusion, diffusion–thermo effects, and chemical reactions under slip and convective boundary conditions. Using similarity transformations, the governing nonlinear partial differential equations are reduced to a system of stiff ordinary differential equations and solved numerically via the spectral quasi-linearization method implemented in MATLAB. The accuracy of the numerical scheme is confirmed through comparisons with previously published results, showing strong agreement. The findings reveal that the temperature decreases with increasing Prandtl number and thermal slip, while higher values of the chemical reaction, Dufour, and velocity slip parameters collectively diminish the concentration field.

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 score0.303
Threshold uncertainty score0.371

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 head0.217
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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