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Record W4381619307 · doi:10.11159/ffhmt23.127

Natural Rayleigh Benard Convection Of Bingham Fluid In Enclosure Cavity With Sinusoidal Profiles

2023· article· en· W4381619307 on OpenAlexvenueno aff
Keddar Mohammed, Draoui Belkacem, Brahim Mebarki, Medale Marc

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsEnclosureNatural convectionMechanicsRayleigh numberRayleigh–Bénard convectionConvectionMaterials sciencePhysicsComputer science

Abstract

fetched live from OpenAlex

In this study, two-dimensional steady-state simulations of laminar natural convection of Rayleigh Benard in square enclosure were performed.The enclosure is considered to be completely filled with a yield stress fluid obeying viscoplastic model Bingham.The vertical lateral walls are thermally isolated, whereas sinusoidal temperature distributions with different amplitudes and phases are imposed over the horizontal walls.Fluid flow and heat transfer characteristics are systematically studied over a wide range of Phase deviation ϕ (0-π) and amplitude ration Ɛ (0-1).We have fixed the Rayleigh number (Ra = 10 5 ), Prandtl number (Pr = 7), and finally the Bingham number (Bn = 0.5).The Navier-Stokes equations, the mass and energy conservation equations, are solved numerically using CFD software FLUENT 15.The results shows that the Nusselt number decreases with the increase of the Bingham number, and for the large values of the latter the heat transfer is done by conduction.It is also noteworthy that the increase in the phase difference and the amplitude ratio leads to the increase in the heat transfer.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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