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Record W1996885151 · doi:10.1115/fedsm2012-72301

Investigation of the Turbulent Flow Behaviour in a Transpired Air Collector

2012· article· en· W1996885151 on OpenAlexaff
David Greig, Kamran Siddiqui, Panagiota Karava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsWestern University
FundersCore Research for Evolutional Science and Technology
KeywordsTurbulenceParticle image velocimetryAirflowMechanicsTurbulence kinetic energyReynolds stressReynolds numberMeteorologyClear-air turbulenceHeat transferEnvironmental scienceFlow (mathematics)Materials sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

There is an increasing interest in developing renewable energy systems to address the increasing global energy demand and fight climate change. One emerging technology is the transpired air collector, which is a unique type of corrugated and perforated sheet metal installed in front of a building to absorb incident sunlight to preheat the building air intake. As the airflow behaviour in the channel influences the air heat gain, it is important to understand the fluid dynamics within the transpired air collector to maximize its efficiency. A full scale experimental setup using a commercial transpired air collector was built in a laboratory environment. Particle Image Velocimetry (PIV) was used to measure two-dimensional velocity fields at different air flow rates and at different locations inside the channel. PIV data were used to compute various turbulent characteristics of the air flow. It was found that the mean velocity peaks tended towards the flat construction wall side. The profiles of the Reynolds stress indicated a significant momentum transfer from the corrugation wall by the turbulent velocity field towards the bulk flow. Results demonstrate that the turbulence produced by the corrugation waveform dominates the entire channel.

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 score0.048
Threshold uncertainty score0.213

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 head0.016
GPT teacher head0.192
Teacher spread0.176 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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