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MODELS FOR PRESSURE DROP AND HEAT TRANSFER IN AIR COOLED COMPACT WAVY FIN HEAT EXCHANGERS

2011· article· en· W2039780870 on OpenAlexaff
Mohamed M. Awad, Yuri S. Muzychka

Bibliographic record

VenueEnhanced heat transfer/Journal of enhanced heat transfer · 2011
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReynolds numberMechanicsFinPressure dropPrandtl numberHeat transferHeat exchangerBoundary layerLaminar flowMaterials scienceThermodynamicsAnnular finPhysicsHeat transfer coefficientTurbulence

Abstract

fetched live from OpenAlex

A detailed review and analysis of the thermal-hydrodynamic characteristics in air-cooled compact wavy fin heat exchangers is presented. New models are proposed which simplify the prediction of the Fanning friction factor f and the Colburn j factor. These new models are developed by combining the asymptotic behavior for the low Reynolds number and laminar boundary layer regions. In these two regions, the models are developed by taking into account the geometric variables such as: fin height (H), fin spacing (S), wave amplitude (A), fin wavelength (λ), Reynolds number (Re), and Prandtl number (Pr). The proposed models are compared with numerical and experimental data for air at different values of the geometric variables obtained from the published literature. The new models for f and j cover a wide range of the Reynolds number. Since the model is based analytically, it will also allow for proper design assessment of heat exchanger performance.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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 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

Citations47
Published2011
Admission routes1
Has abstractyes

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