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Record W2604889855 · doi:10.1051/epjap/2017170087

Study on an antagonist differentiated heated lid driven-cavity enclosing a tube: lattice Boltzmann method

2017· article· en· W2604889855 on OpenAlexaff
Xiaoyan Ma, Nicolas Pellerin, Marcelo Reggio, Rachid Bennacer

Bibliographic record

VenueThe European Physical Journal Applied Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNusselt numberLattice Boltzmann methodsMechanicsReynolds numberNatural convectionHeat transferCylinderPhysicsRayleigh numberClassical mechanicsGeometryMathematicsTurbulence

Abstract

fetched live from OpenAlex

The method of lattice-Boltzmann multiple relaxation time (MRT) is commonly applied to study the conversion system consisting in a combination of forced convection and natural convection occurred in a cavity. Moving the top surface horizontally at a fixed speed, while two vertical walls are applied with constant different temperatures, assuming adiabatic case on both bottom and top walls. We consider a “non-cooperating” situation, where dynamics and buoyancy forces counterbalance. The cavity contains a circular cylinder placed at various positions. Boundary conditions for velocity and temperature have been applied to handle the non-Cartesian boundary of the cylinder. In lattice Boltzmann methods we adopt the double distribution model for calculating both the thermal and hydrodynamic fields. The D2Q5 and D2Q9 lattice are chosen to perform the simulations for a wide range of Reynolds and Rayleigh numbers. By calculating the average Nusselt number, we also investigated the influence of different obstacle positions on characteristics of flow and heat transfer. The results show the influence of the obstacle position on the dimensionless numbers, so as to effect the heat transfer behaviors inside the cavity. It is also indicates that the governing parameters are also related to driven power for the upper surface sliding.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.317
Teacher spread0.274 · 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.

Study designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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