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Record W2807549492 · doi:10.1115/1.4040412

Numerical Simulations of Natural Convective Heat Transfer in Vertical Concentric and Eccentric Annular Channels

2018· article· en· W2807549492 on OpenAlexaff
A. A. Busedra, Stavros Tavoularis

Bibliographic record

VenueJournal of Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNusselt numberMechanicsConcentricHeat transferAdiabatic processHeat fluxNatural convectionMaterials scienceConvective heat transferConvectionAspect ratio (aeronautics)Adiabatic wallThermodynamicsGeometryPhysicsTurbulenceComposite materialReynolds numberMathematics

Abstract

fetched live from OpenAlex

Natural convective heat transfer in a concentric and a highly eccentric, vertical, open ended, annular channel has been investigated numerically. The inner to outer diameter ratio was 0.61, and the height to hydraulic diameter ratio was 18:1. Three heating modes were considered, all having uniform heat flux applied to one or both of the two walls, while the unheated wall was kept adiabatic. The wall temperature distribution, mass flow rate, and midchannel Nusselt number for the case with both walls heated were found to be in excellent agreement with available experimental results. For the same heating conditions, the heat transfer rate in the concentric annular channel was found to be greater than that in the highly eccentric channel, while the mass flow rate was higher in the eccentric channel. A novel finding for the eccentric channel was that the location of maximum velocity was intermediate between the narrow and wide gaps. Another novel observation, which was attributed to radiation effects, was that the fluid temperature in the wide gap region was lower than that of an adiabatic wall. The paper contains additional observations that would be of interest to designers of systems containing annular channels.

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 score0.121
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.232
Teacher spread0.221 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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