MétaCan
Menu
Back to cohort
Record W4281853998 · doi:10.3390/fluids7060195

Thermohydraulic Performance of Chevron Pin-Fins

2022· article· en· W4281853998 on OpenAlexafffund
M. Ziad Saghir, Ibrahim Fathi Ghalayini

Bibliographic record

VenueFluids · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChevron (anatomy)Pressure dropFinMechanicsMaterials scienceLaminar flowReynolds numberDarcy–Weisbach equationPorosityDarcy numberPorous mediumThermodynamicsTurbulenceGeologyComposite materialPhysicsNusselt number

Abstract

fetched live from OpenAlex

The present study focuses on the optimum design effectiveness in heat removal for small surfaces. Pin-fin made of solid and porous cylindrical shape forming chevron is investigated numerically using the finite element method. The design consists of 3-chevron and 5-chevron configurations connected to a heated block with fluid circulating between the chevron and above them. Variable Reynolds number and pin-fins height ranging from 2 mm to 8 mm are investigated. The full Navier–Stokes equation combined with the energy equation was solved in the presence of the solid pin-fins. The Darcy–Brinkman model with the effective energy equation is used in the presence of the porous pin-fins. The system is solved for Reynolds numbers ranging from 50 to 1000, thus remaining in the laminar regime. Results revealed that the best performance evaluation criterion is higher for the 8 mm porous pin-fins regardless of their permeability. If one ignores the pressure drop and friction contribution, a solid pin-fin having a height of 4 mm showed the best heat absorption mechanism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFluidsSame topicHeat Transfer and OptimizationFrench-language works237,207