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Record W2963858427 · doi:10.1139/cjp-2019-0055

Multiple-relaxation-time lattice Boltzmann simulation of natural convection with multiple heat sources in a rectangular cavity

2019· article· en· W2963858427 on OpenAlexvenueno aff
Peisheng Li, Xiaolong Lian, Ying Zhang, Wandong Zhao, Chunyang Ma

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLattice Boltzmann methodsHeat transferMechanicsPhysicsOffset (computer science)Nusselt numberNatural convectionConvectionThermodynamicsReynolds number

Abstract

fetched live from OpenAlex

Natural convection and heat transfer in a square cavity with multiple heat sources was investigated through a multiple-relaxation-time (MRT) collision model and lattice Boltzmann method (LBM) in the current work. The MRT-LBM model was verified by a former experiment and numerical findings with different Ra numbers from 10 3 to 10 5 , which proved the MRT-LBM model is effective to handle the flow and transfer. The heat transfer that developed inside the cavity was analyzed under different width, height, and lateral offset of heat source in this paper. Moreover, the change of spacing between two symmetrically distributed heat sources was discussed. The results showed that the heat exchange efficiency was augmented by increasing width, height, and spacing of the heater, but it was reduced by increasing lateral offset. Specifically, the Nusselt number of the upper wall decreased by increasing height of heat source, and the left and right walls showed better heat exchange efficiency by increasing height. Additionally, the lateral position had a notable influence on the left wall surface of the heat source, and the optimum heat exchange efficiency of the heat source’s left wall existed in the condition of small lateral offset.

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 score0.030
Threshold uncertainty score0.592

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 head0.008
GPT teacher head0.204
Teacher spread0.197 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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