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Record W2160110021 · doi:10.1115/1.4026218

Free Convection in Asymmetrically Heated Vertical Channels With Opposing Buoyancy Forces

2013· article· en· W2160110021 on OpenAlexaff
Derek Roeleveld, David Naylor, Wey H. Leong

Bibliographic record

VenueJournal of Heat Transfer · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNusselt numberPrandtl numberBuoyancyMechanicsLaminar flowRayleigh numberFlow visualizationStreamlines, streaklines, and pathlinesNatural convectionMaterials scienceHeat transferThermodynamicsTurbulenceFlow (mathematics)Reynolds numberPhysics

Abstract

fetched live from OpenAlex

Laser interferometry and flow visualization were used to study free convective heat transfer inside a vertical channel. Most studies in the literature have investigated buoyancy forces in a single direction. The study presented here investigated opposing buoyancy forces, where one wall is warmer than the ambient and the other wall is cooler than the ambient. An experimental model of an isothermally, asymmetrically heated vertical channel was constructed. Interferometry provided temperature field visualization and flow visualization was used to obtain the streamlines. Experiments were carried out over a range of aspect ratios between 8.8 and 26.4, using temperature ratios of 0, −0.5, and −0.75. These conditions provide a modified Rayleigh number range of approximately 5 to 1100. In addition, the measured local and average Nusselt number data were compared to numerical solutions obtained using ANSYS FLUENT. Air was the fluid of interest. So the Prandtl number was fixed at 0.71. Numerical solutions were obtained for modified Rayleigh numbers ranging from the laminar fully developed flow regime to the turbulent isolated boundary layer regime. A semi-empirical correlation of the average Nusselt number was developed based on the experimental data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.383

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 head0.006
GPT teacher head0.184
Teacher spread0.177 · 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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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