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Record W4401958366 · doi:10.3390/app14177594

Experimental Forced Convection Study Using a Triply Periodic Minimal Surface Porous Structure with a Nanofluid: Comparison with Numerical Modeling

2024· article· en· W4401958366 on OpenAlexaff
M. Ziad Saghir, Gülenay Alevay Kılıç

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNanofluidMaterials scienceHeat sinkNusselt numberPorosityHeat fluxMechanicsForced convectionThermodynamicsConvectionComposite materialHeat transferNanoparticleNanotechnologyPhysicsReynolds number

Abstract

fetched live from OpenAlex

Triply periodic minimal surfaces (TPMSs) show potential as porous materials in different engineering applications. Amongst them, heat sink is the subject of this paper. The advantage of such a structure is the ability to design it based on the intended applications. In the present paper, an attempt is made to experiment with a better understanding of the performance of TPMSs in heat sink applications. The experiment was conducted for different flow rates, and two heat sink materials, aluminum and silver, were used. In addition, two fluids were used experimentally: The first was water, and the second was a mixture of water containing 0.6% aluminum nanoparticles and identified as a nanofluid. The applied heat flux was maintained constant at 30,800 W/m2. The results reveal experimentally and confirm numerically that the TPMS structure secures a uniform heat extraction in the system. The development of the boundary layer in the porous structure is reduced due to the current structure design. A higher Nusselt number is obtained when the nanofluid is used as the circulating fluid. The performance evaluation criteria in the presence of the nanofluid exceed 100.

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 score0.153
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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