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Record W4281853998 · doi:10.3390/fluids7060195

Thermohydraulic Performance of Chevron Pin-Fins

2022· article· en· W4281853998 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 score0.069
Threshold uncertainty score0.602

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)0.0010.000

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