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FLOW VISUALIZATION OF NATURAL CONVECTION IN A VERTICAL CHANNEL WITH ANTISYMMETRIC HEATING

2012· article· en· W2035367188 on OpenAlexaff
Derek Roeleveld, David Naylor

Bibliographic record

VenueJournal of Flow Visualization and Image Processing · 2012
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMechanicsBuoyancyFlow visualizationNatural convectionLaminar flowMaterials scienceTurbulenceInstabilityFlow (mathematics)ConvectionOpticsMeteorologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Flow- and temperature-field photographs were obtained for natural convection in a vertical channel with antisymmetric heating. The heating is antisymmetric when the hot wall is heated above the ambient temperature by the same amount that the cold wall is cooled below the ambient, producing equal but opposite buoyancy forces. These opposite buoyancy forces can cause instability inside the channel. So the flow- and temperature fields are of interest for validation of numerical modeling. The flow field was obtained by means of smoke flow visualization using a laser sheet and a Drager tube, which supplied the aerosol. A Mach−Zehnder interferometer was used to obtain the temperature field. Results were obtained over a range of aspect ratios and Rayleigh numbers ranging from a steady laminar to an unsteady turbulent flow.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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