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Record W2963892784 · doi:10.1080/10407782.2019.1638169

Effect of thermal boundary condition on forced convection from circular cylinders

2019· article· en· W2963892784 on OpenAlexaff
Mohamed Abdelhady, David Wood

Bibliographic record

VenueNumerical Heat Transfer Part A Applications · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNusselt numberPrandtl numberReynolds numberMechanicsForced convectionHeat fluxFilm temperatureThermodynamicsHeat transfer coefficientHeat transferTurbulent Prandtl numberMaterials sciencePhysicsTurbulence

Abstract

fetched live from OpenAlex

Researchers commonly assume that the effect of constant temperature and constant heat flux thermal boundary condition on forced convection from a circular cylinder is negligible. Such claim was assessed using Computational Fluid Dynamics for Reynolds number, Re, ≤ 3,000. The flow was directly solved without modeling for Re ≤ 300, and Large Eddy Simulation was used for Re = 1,000 and Re = 3,000. Constant property simulations were done for Prandtl number of 0.7. Fluid dynamics parameters: drag coefficient, Strouhal number, separation angle and recirculation length, were used to assess the accuracy of the simulations. The main parameter of interest, the Nusselt number, is higher locally for constant heat flux than constant temperature for 60 ≤ Re ≤ 3,000. In addition, the ratio of overall Nusselt number for constant heat flux to that for constant temperature increases for Re < 30 and is ∼1.15 between 30 ≤ Re ≤ 3,000, showing that thermal boundary condition has significant effect on heat transfer from circular cylinder. Finally, the study developed empirical correlations relating overall Nusselt number to studied Reynolds number range.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.004
GPT teacher head0.221
Teacher spread0.216 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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