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NATURAL CONVECTION OF NANOFLUIDS IN HEATED ENCLOSURES USING THE LATTICE BOLTZMANN METHOD

2011· article· en· W2065146442 on OpenAlex
J賴me Guiet, Marcelo Reggio, P. Vasseur

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsNanofluidLattice Boltzmann methodsNatural convectionMaterials scienceMechanicsConvectionThermodynamicsHeat transferPhysics

Abstract

fetched live from OpenAlex

The lattice Boltzmann method applied on two-dimensional Cartesian meshes has been used to investigate the solution of the natural convection of nanofluids in shallow cavities. This kind of flow problem was implemented to solve temperature, vorticity, and stream function equations under the Boussinesq approximation, with a specific treatment for nanoparticles. Validations were first conducted by studying the natural convection of ordinary fluids in square cavities for a range of Rayleigh numbers between 103 and 106 with a Prandtl number of 0.71. The results were compared with data available in the literature. Once satisfactory results were found for this test problem, the lattice Boltzmann formulation was applied to study natural convection involving nanofluids. The influence of nanoparticles on convectional flows in shallow cavities heated from below was simulated for various Rayleigh numbers. This study was conducted with water-based nanofluids for different kinds and various ratios of nanoparticles. Good agreement was found when these results were compared with analytical data given in the literature.

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.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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