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Record W4391046002 · doi:10.24200/sci.2024.61513.7348

Rotational Stagnation Point Non-Newtonian Second-Grade Fluid Flowing over Spiraling Disk

2024· article· en· W4391046002 on OpenAlexaff
A. Abbasi, W. Farooq, Fazle Mabood, Asma Tariq, Sabir Ali Shehzad, Irfan Anjum Badruddin

Bibliographic record

VenueScientia Iranica · 2024
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
FundersKing Khalid University
KeywordsMechanicsStagnation pointNusselt numberNewtonian fluidViscoelasticityPoint (geometry)Rotational speedBoundary layerNon-Newtonian fluidParasitic dragMaterials sciencePhysicsClassical mechanicsHeat transferThermodynamicsMathematicsReynolds numberGeometry

Abstract

fetched live from OpenAlex

Numerical study is conducted to execute the analysis of rotational stagnation point second-grade liquid flowing over the spiraling rotatory disk. Heat transmission analysis is accounted. The problem is formulated in the coupled partial differential equations forms which are later simplified in view of similar variables. The Keller-Box (KB) procedure is adopted for the execution of numerical solutions. The involved parameters influences on the velocity and temperature profiles are presented and interpreted. The skin-frictions and Nusselt number are reported in the forms of numerical data. The present results are verified through the comparison with already available material in the literature. This study addressed that the thickness layer of boundary augmented against the incrementing viscoelastic and rotational parameters. Both rotational and viscoelasticity resist the temperature. The rotational parameter rises radial skin-friction and heat transmission rate while diminishes the swirl skin friction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.228
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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