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Record W4308876851 · doi:10.3390/fluids7110348

Enhanced Energy Storage Using Pin-Fins in a Thermohydraulic System in the Presence of Phase Change Material

2022· article· en· W4308876851 on OpenAlexafffund
M. Ziad Saghir

Bibliographic record

VenueFluids · 2022
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat transferMaterials sciencePhase-change materialFinThermal energy storageEnhanced heat transferConvectionConvective heat transferMechanicsHeat transfer enhancementNatural convectionEnergy storageHeat transfer coefficientPhase changeThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Energy storage has been an essential topic in thermal management. With the low conductivity of phase change material, the effort is to propose the best mechanism for heat transfer. In the present paper, pin-fins are used in the hydraulic system to transfer the heat coming from wastewater management into phase change material. Different flow rates have been tested, and it was found that pin-fins can create mixing in the flow chamber allowing a large convective heat flux to move heat into the phase change material. In the present design, it was found that natural convection assists in heat transfer. Additional findings suggested that the pin-fins height influence the heat transfer process. In the current configuration, 5 mm in height pin-fins demonstrated the best heat transfer when compared to pin-fins varying from 1 mm to 6 mm, respectively.

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 communication0.0000.001
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 head0.057
GPT teacher head0.296
Teacher spread0.239 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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