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Record W2921754471 · doi:10.1139/cjp-2018-0862

Lattice Boltzmann simulation of conjugate forced convection in a channel heat sink with surface-mounted blocks

2019· article· en· W2921754471 on OpenAlexvenueno aff
Haifeng Zhang, Dinggen Li, Peixin Ye, Zihao Yu

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsNusselt numberHeat transferForced convectionMechanicsLattice Boltzmann methodsStreamlines, streaklines, and pathlinesPhysicsPressure dropThermal conductivityReynolds numberHeat sinkThermodynamicsThermal conductionTurbulence

Abstract

fetched live from OpenAlex

The study of the conjugate forced convection in a channel has many practical applications and has attracted attention from researchers, although the conjugate heat transfer in this configuration is usually ignored. In this paper, the conjugate forced convection heat transfer in a channel heat sink with surface-mounted blocks is numerically studied with the lattice Boltzmann method. The effects of Reynolds numbers and geometrical parameters of the blocks in different aspect ratios on the flow field and temperature distribution for various thermal conductivity ratio of solid wall to the fluid are analyzed. The results reveal that the distributions of the vortices and streamlines in the channel heat sink largely depend on the geometric parameters, and the increase of the distance between two mounted blocks tends to cause the pressure drop to increase and the average Nusselt number decreases. In addition, we found that a modification of the thermal conductivity ratio of solid to fluid has little effect on the pressure drop, whereas the heat transfer performance becomes much better.

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 score0.005
Threshold uncertainty score0.578

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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