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Record W4390670924 · doi:10.28924/2291-8639-22-2024-7

Effects of Rotation and Magnetic Field on Rayliegh Benard Convection

2024· article· en· W4390670924 on OpenAlexvenueno aff
Abdelfatah Abasher, Elsiddeg Ali, H. S. Adam

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersTaif University
KeywordsMathematicsRayleigh numberMagnetic fieldEigenvalues and eigenvectorsBoundary value problemRotation (mathematics)Mathematical analysisConvectionRayleigh–Bénard convectionRayleigh scatteringPerturbation (astronomy)Chebyshev polynomialsClassical mechanicsMechanicsPhysicsGeometryNatural convection

Abstract

fetched live from OpenAlex

In this paper, a numerical method based on the Chebyshev tau method is applied to analyze the effects of rotation and magnetic fields on Rayleigh-Bénard convection. The rotation and magnetic fields are assumed to be parallel to the vertical direction. The perturbation equations and boundary conditions are analyzed using normal mode analysis. The equations are then converted into a non-dimensional form and transformed into a generalized eigenvalue problem of the form AX=RBX, where R represents the eigenvalue corresponding to the Rayleigh number. The MATLAB software package is utilized to determine the relationship between the Rayleigh number and the Taylor number (rate of rotation), as well as the relationship between the Rayleigh number and the magnetic parameter (strength of the magnetic field) for different boundary conditions (free-free, rigid-rigid, or one free and the other rigid). The numerical and graphical results are presented and found to be in full agreement with the results obtained from previous analytical and numerical studies of the problem.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.002
GPT teacher head0.210
Teacher spread0.208 · 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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