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Record W2805486840 · doi:10.1139/tcsme-2017-0129

Experimental investigation of forced convection heat transfer over horizontal tube with conical fins for different fin spacings and different inclination angles

2018· article· en· W2805486840 on OpenAlexvenueno aff
Gülay Yakar

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNusselt numberFinConical surfaceMaterials scienceHeat transferPressure dropMechanicsForced convectionInclination angleGeometryPhysicsReynolds numberComposite materialTurbulenceMathematics

Abstract

fetched live from OpenAlex

This research was performed to experimentally study the heat transfer over a horizontal tube with conical fins for different fin spacings and different inclination angles. The air entering the test section first contacts the large surface of the conical fin. Therefore the conical fin both directs the air towards the heating surface and increases the heat transfer surface area. The experiments were conducted for three different inclination angles (45°, 60° and 80°) and three different fin spacings (10, 12, and 15 mm). Water and air were used as hot and cold fluid in these experiments, respectively. The water temperature, which was the heating fluid, was kept fixed at 65 °C. The cold fluid entered the test section at eight different air flow velocities (2–20 m/s). Experimental results indicated that the Nusselt number at each inclination angle for the 10 mm fin spacing was almost the same. Moreover, for the 15 mm fin spacing, the highest Nusselt number was obtained at 80° and lowest at 60° for Re > 5 × 10 4 , while the Nusselt number was almost the same at each inclination angle for Re < 5 × 10 4 . It was determined that the lowest pressure drop was obtained at 80° for both 10 and 15 mm.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.663

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)0.0000.000

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

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