Flow Visualization of Airflow Through a Rectangular Duct With Combined Heat and Mass Transfer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".