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Record W2223852565 · doi:10.4271/2004-01-0217

An Elliptical Tube Array as a Cross Flow Heat Exchanger

2004· article· en· W2223852565 on OpenAlexaff
Mesbah G. Khan, Amir Fartaj, David S.‐K. Ting

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHeat exchangerTube (container)MechanicsFlow (mathematics)Materials scienceMechanical engineeringPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Airside heat transfer and fluid flow characteristics of a single array cross flow heat exchanger, made of elliptical tubes, were studied. The heat exchanger, consists of 18 tubes each of 30 cm long with 0.30 minor to major outside axis ratio and equally spaced by 0.61 cm gap, was oriented in a 30 cm by 30 cm test section of a wind tunnel with the major axis parallel to the air flow. Tests were performed with hot water flowing inside the tubes, while cold air flowing across them externally. The mean temperature difference between the approaching air and the surface of the tubes was maintained at roughly 14±2°C. Reynolds number based on the mean free stream air velocity and hydraulic diameter of the elliptical tube was varied from 4500 to 15000, while that based on the mean water velocity inside the tube was altered from 1400 to 7400. The results showed that the heat transfer rate increased with the airflow rate in a power law relationship, giving an average Nusselt number on the airside, which varied with the corresponding Reynolds number also in a power law manner. The results were compared with previously established similar studies and correlations.

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 score0.002
Threshold uncertainty score0.005

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.246
Teacher spread0.237 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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