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Record W1492897895 · doi:10.1115/fedsm2013-16493

Effect of Low Reynolds Number Mixed Convection on the Flow Development Inside Channel

2013· article· en· W1492897895 on OpenAlexaff
Ahmed Elatar, Kamran Siddiqui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsGrashof numberTurbulenceReynolds numberMechanicsBuoyancySecondary flowOpen-channel flowCombined forced and natural convectionFlow (mathematics)PhysicsConvectionMaterials scienceNatural convectionNusselt number

Abstract

fetched live from OpenAlex

The development of low Reynolds number channel flow during mixed convection has been investigated experimentally. The measurements were taken at five different locations along the heating section of the channel. The experiments were conducted at bottom wall temperatures of 35, 45 and 55 °C at a flow rate of 0.0315 kg/s (corresponding to the unheated Reynolds number of 450). Grashof number ranged from 9.8 × 106 to 3.9 × 107. The results showed that the buoyancy-driven secondary flow was generated right from the upstream tip of the channel heated section and was enhanced in the downstream direction. Accordingly, turbulence was generated and enhanced in the same direction. The mean streamwise velocity accelerated in the region close to the bottom heated wall in the downstream direction and the rate of acceleration increased with an increase in the bottom wall temperature. The turbulent streamwise and vertical velocities approximately reached close to the development state near the end of the channel heated section for the lowest bottom wall temperature while at the higher two bottom wall temperatures, the turbulent velocities were found to progress along the channel heated section.Copyright © 2013 by ASME

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 categoriesInsufficient payload (model declined to judge)
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 score0.259
Threshold uncertainty score1.000

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)0.0010.001

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 head0.007
GPT teacher head0.199
Teacher spread0.193 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2013
Admission routes1
Has abstractyes

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