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Record W3109329526 · doi:10.1080/02286203.2020.1843934

Fractional derivative modeling of double-diffusive free convection with von Neumann stability analysis

2020· article· en· W3109329526 on OpenAlexaff
Mohamed F. El‐Amin, Ahmed G. Radwan, Jisheng Kou, Amgad Salama, Shuyu Sun

Bibliographic record

VenueInternational Journal of Modelling and Simulation · 2020
Typearticle
Languageen
FieldMathematics
TopicFractional Differential Equations Solutions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsNusselt numberConvective heat transferSherwood numberThermodynamicsMechanicsFractional calculusHeat transferMomentum (technical analysis)Derivative (finance)ConvectionMaterial derivativeStability (learning theory)MathematicsMathematical analysisPhysicsTurbulenceReynolds number

Abstract

fetched live from OpenAlex

This paper focuses on the problem of fractional time derivative of fluid flow and convective heat and mass transfer from a heated semi-infinite wall immersed. We provided two cases of study, one is free convective heat transfer and the other is a free double-convective heat and mass transfer. The time-derivative terms in the equations of momentum, energy and concentration are assumed to be fractional using the Grunwald-Letnikov (GL) model. A finite difference scheme has been developed for each case of study and followed by a von Neumann stability analysis. Therefore, a stability condition has been derived for each case of study. It was found that the derived stability condition collapses to the traditional one as the derivative order equals one, which reflects an accurate implementation. Also, selected physical results have been presented for the velocity, temperature and concentration profiles as well as variations of Nusselt/Sherwood number and friction coefficient.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.147
GPT teacher head0.344
Teacher spread0.197 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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