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Record W4294869883 · doi:10.18280/ijdne.170405

Investigation of Temperature Distribution in a Slab Using Lattice Boltzmann Method

2022· article· en· W4294869883 on OpenAlexvenueno aff
Moses Omolayo Petinrin, Al-Amin Owodunni, Rasaq A. Kazeem, Omolayo M. Ikumapayi, Sunday A. Afolalu, Esther T. Akinlabi

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSlabLattice Boltzmann methodsMultiphysicsFinite element methodBoundary value problemBoltzmann equationLattice (music)Boltzmann distributionMechanicsPhysicsDistribution (mathematics)Statistical physicsMaterials scienceMathematical analysisMathematicsThermodynamics

Abstract

fetched live from OpenAlex

In this paper, the temperature distribution in a slab was investigated. A model based on the Boltzmann transport equation without heat source was simplified using the Bhatnagar-Gross-Krook (BGK) approximation was applied. This is an example of the Lattice Boltzmann Method. The model was developed based on using a D2Q4 lattice arrangement for the medium of study. To obtain results, the model was tested on different cases: Two box-shaped slabs with different boundary conditions, and a T-shaped and an L-shaped slabs to determine the temperature distributions different times t > 0. The results obtained based on the developed model were validated with the enterprise software COMSOL Multiphysics which is based on the Finite Element Method. For the two cases of box-shaped and the T-shaped slabs, their results were in nearly perfect agreement with the finite element method. However, for the L-shaped slab, there was good agreement at most points apart from the regions where there was change of shape. In conclusion there is high agreement between the results of LBM and using COMSOL, which proves that LBM can be used to determine temperature distribution in a slab accurately.

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 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 score0.051
Threshold uncertainty score0.436

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 science0.0000.000
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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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
Published2022
Admission routes1
Has abstractyes

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