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Record W3211852909 · doi:10.1115/imece2000-1409

A Study of Three-Dimensional Mixed Convective Heat Transfer From Short Vertical Cylinders in a Horizontal Forced Flow

2000· article· en· W3211852909 on OpenAlexaff
David A. Scott, Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGrashof numberMechanicsForced convectionNusselt numberBuoyancyHeat transferCombined forced and natural convectionConvective heat transferHeat transfer coefficientReynolds numberThermodynamicsNatural convectionChurchill–Bernstein equationMaterials sciencePhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract Heat transfer from relatively short vertical isothermal cylinders in a horizontal forced fluid flow has been considered. The flow conditions are such that the buoyancy forces resulting from the temperature differences in the flow are in general significant despite of the presence of a horizontal forced flow of air, that is, mixed convective flow exists. Because the cylinders are short and the buoyancy forces act normal to the forced flow, three-dimensional flow exists. The experiments were performed in a low velocity, open jet wind tunnel. The study involved the experimental determination of the mean heat transfer coefficient and a comparison of the results with a previous numerical analysis. Mean heat transfer rates were determined using the ‘lumped capacity’ method. The mean Nusselt number has the Reynolds number, Grashof number and the height to diameter ratio of the cylinders as parameters. The results have been used to determine the conditions under which the flow departs from purely forced convection and enters the mixed convection regime, i.e., determining the conditions for which the buoyancy effects should be included in convective heat transfer calculations for short cylinders.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.996

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)0.0040.000

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 head0.015
GPT teacher head0.220
Teacher spread0.204 · 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.

Study designObservational
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
Published2000
Admission routes1
Has abstractyes

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