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Record W4386255040 · doi:10.1080/10407782.2023.2251092

Thermal examination of chemical interaction and thermophoretic diffusion of Williamson fluid flow across Riga Plate surface with nonlinearity radiation flux

2023· article· en· W4386255040 on OpenAlexaff
MD. Shamshuddin, S.O. Salawu, Faisal Shahzad, Wasim Jamshed, Mohamed R. Eid, Govind R. Rajput

Bibliographic record

VenueNumerical Heat Transfer Part A Applications · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsDimensionless quantityMechanicsFlow (mathematics)ViscosityPartial differential equationDiffusionThermodynamicsMass transferOrdinary differential equationFluid dynamicsNonlinear systemHeat transferThermophoresisThermalMass fluxPhysicsMathematicsMathematical analysisDifferential equationNanofluid

Abstract

fetched live from OpenAlex

The purpose of this research is to investigate the thermophoretic diffusion and chemical reaction on steady magnetized Williamson fluid flow, heat, and mass transfer along a Riga surface in the presence of variable thermal properties. By imposing the appropriate similarity technique, the system of higher-order formulas has been transformed from partial order into ordinary order. An efficient numerical technique named the classical Keller box scheme has been incorporated to solve these dimensionless coupled non-linear ordinary differential equations. The obtained results are visualized graphically with physical justification. In addition to that, we validated the obtained outcomes by comparing them to earlier literature, and we identified an excellent match, supporting the reliability of the present method. We discovered from our analysis that acceleration in the values of Williamson fluid and viscosity leads to enhancement in both velocity plots. Also, the material and stretching parameters have a major role in the fluid flow control process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations37
Published2023
Admission routes1
Has abstractyes

Explore more

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