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Record W2041659355 · doi:10.1115/ihtc14-22036

Measurement of Time-Averaged Turbulent Free Convection Using Laser Interferometry

2010· article· en· W2041659355 on OpenAlexaff
E. M. Poulad, David Naylor, Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's UniversityToronto Metropolitan University
Fundersnot available
KeywordsEnclosureNusselt numberTurbulenceOpticsInterferometryParticle image velocimetryHeat transferNatural convectionConvectionConvective heat transferPhysicsMaterials scienceMechanicsComputer scienceReynolds number

Abstract

fetched live from OpenAlex

Laser interferometry has been combined with high-speed digital cinematography to measure time-averaged turbulent convective heat transfer rates. The method has been demonstrated for turbulent free convection in a tall vertical enclosure filled with air. Sample results have been obtained for a Rayleigh number (based on the enclosure width) of 2.8×105 and an enclosure aspect ratio of 17.1. An automated digital image processing algorithm has been used to calculate the instantaneous local heat flux from the sequence of interferograms captured by a high-speed camera. The effects of key experimental parameters, such as the camera frame rate and the total image capture time, have been investigated. The average Nusselt number for the entire enclosure was found to compare well with a widely used empirical correlation from the literature.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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 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
Published2010
Admission routes1
Has abstractyes

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