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Record W3176053434 · doi:10.1139/cjp-2021-0021

Numerical simulation of natural convection in a porous cavity with internal hot and cold sources using the lattice Boltzmann method

2021· article· en· W3176053434 on OpenAlexvenueno aff
Ying Zhang, Xuhui Huang, Yichen Huang, Meng Xu, Jie Lei

Bibliographic record

VenueCanadian Journal of Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferLattice Boltzmann methodsNatural convectionThermodynamicsRayleigh numberPhysicsMechanicsIsothermal processHeat exchangerConvection

Abstract

fetched live from OpenAlex

Based on the non-orthogonal multiple-relaxation-time lattice Boltzmann method (MRT–LBM), natural convection in a porous square cavity with a pair of isothermally hot and cold blocks inside was studied numerically in the current study. The influence of arrangements (Cases 1, 2, 3, 4, and 5), spacing ratio (S), and size ratio (A) of the hot and cold sources and the Rayleigh number (Ra) on the heat exchange efficiency were studied. The results show that different arrangements produce different heat-transfer effects. Two arrangements, hot and cold blocks placed horizontally (Case 1) and a cold block located in the upper left corner while a hot block is located in the bottom right corner (Case 4), have better heat exchange performance than the other three cases because the flow directions of hot and cold fluids are closer to that of the heat transfer. Then, the influence of the spacing between blocks and the size of the blocks on the heat transfer rate was further studied in Cases 1 and 4. Heat transfer performance improved with increasing A. Additionally, the variation in the heat transfer performance with spacing is related to the arrangement and size ratio of the blocks. For Ra = 10 4 , 10 5 , and 10 6 , the best heat transfer characteristics were obtained in Case 1 when S = 0.05 and A = 0.20. For Ra = 10 7 , Case 4 exhibited the best heat transfer effect when S = 0.35 and A = 0.20.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.282

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 communication0.0000.000
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 head0.020
GPT teacher head0.267
Teacher spread0.248 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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