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Record W2017013576 · doi:10.1115/ihtc14-22881

Flow Visualization of Airflow Through a Rectangular Duct With Combined Heat and Mass Transfer

2010· article· en· W2017013576 on OpenAlexaff
Melanie Fauchoux, Carey J. Simonson, David A. Torvi

Bibliographic record

Venue2010 14th International Heat Transfer Conference, Volume 2 · 2010
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAirflowDuct (anatomy)Heat transferMechanicsFlow visualizationMaterials scienceForced convectionNatural convectionEnvironmental scienceConvectionLouverReynolds numberThermostatMoistureHumidityMeteorologyFlow (mathematics)Mechanical engineeringEngineeringComposite materialTurbulencePhysics

Abstract

fetched live from OpenAlex

Radiant ceiling panels have been shown to provide good thermal comfort for occupants and reduce energy consumption in large buildings. A disadvantage of radiant panels however, is the inability to alter the relative humidity (RH) of a space, which can also lead to occupant discomfort. A new panel is being developed which will transfer both heat and moisture to a room, to moderate space temperature and RH simultaneously. In order to determine how this panel performs, a test panel has been created and tested in an experimental facility. The panel is situated in the top of a rectangular duct. The surface of the panel is made of a porous membrane, which allows moisture to transfer between the air and the panel. Air passes through the duct and underneath the panel, at low Reynolds numbers (Re). As heat and moisture are transferred between the panel and the air, temperature and concentration gradients form in the duct. If these gradients become large, free convection will occur. Depending on which type of convection is dominant, free or forced, the air will flow in different patterns. In order to understand the performance of the panel, flow visualization is used to determine how the airflow is affected by temperature and concentration gradients. Test conditions include heating, cooling, humidification and dehumidification.

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: none
Teacher disagreement score0.564
Threshold uncertainty score0.953

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)0.0010.000

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 head0.208
Teacher spread0.199 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venue2010 14th International Heat Transfer Conference, Volume 2Same topicBuilding Energy and Comfort OptimizationFrench-language works237,207