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NATURAL CONVECTION OF NANOFLUIDS IN A SQUARE CAVITY HEATED FROM BELOW

2013· article· en· W2036129804 on OpenAlexaff
Harold Noriega, Marcelo Reggio, P. Vasseur

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsNanofluidNusselt numberNatural convectionPrandtl numberMaterials scienceStreamlines, streaklines, and pathlinesLattice Boltzmann methodsRayleigh numberThermal conductivityHeat transferMechanicsThermodynamicsReynolds numberPhysicsComposite materialTurbulence

Abstract

fetched live from OpenAlex

This paper reports a numerical study of natural convection in a square cavity filled with a copper-water nanofluid. The simulations are conducted following the lattice Boltzmann approach. The lower and upper horizontal walls of the cavity are heated and cooled isothermally, respectively; the vertical ones are insulated. The thermal conductivity of the nanofluid is modeled according to Maxwell-Garnett (Philos. Trans. R. Soc., vol. A23, pp. 385−420,1904) and Koo and Kleinstreuer (J. Nanopart. Res., vol. 6, pp. 577−588, 2004), respectively. The governing parameters for the problem are the thermal Rayleigh number, the Prandtl number, and the solid volume fraction of nanoparticles. Numerical solutions of the governing equations, based on the lattice Boltzmann method, are obtained for a wide range of the governing parameters. Results are presented in the form of streamlines, isotherms, and Nusselt numbers. The influence of the two models considered here, to predict the thermal conductivity of a nanofluid, on the onset of motion, onset of oscillatory flow, and convective heat transfer, is discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.453
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.287
Teacher spread0.267 · 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 teacher head, 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

Citations8
Published2013
Admission routes1
Has abstractyes

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