The Cold Tube: Membrane assisted radiant cooling for condensation-free outdoor comfort in the tropics
Bibliographic record
Abstract
Abstract Air conditioning demand is projected to increase rapidly over the next 50 years, particularly in already hot and humid climates. Radiant cooling can be an energy efficient strategy to mitigate comfort energy demand with high air temperatures, thereby reducing both sensible and latent loads in spaces. We have built an outdoor radiant cooling pavilion, the Cold Tube, which is able to produce a mean radiant temperature up to 10 °C below the air temperature in hot and humid Singapore. It avoids condensation and unwanted air cooling by separating cold surfaces from the outside air with a membrane transparent to the radiant cooling heat transfer. This strategy eliminated unwanted convective losses in the form of sensible (air conditioning) and latent (condensation) losses. Controlling the system to avoid condensation was a major feature of the research, and the results show that as cooling demand increases due to warmer air temperatures, the cooling capacity of the Cold Tube also increased to compensate, providing comfortable setpoints to all measured ambient conditions over the duration of the experiment. For ambient air conditions on site in Singapore of 31°C and 65 %RH, we were able to maintain a 22°C mean radiant temperature inside of the pavilion. The additional cooling increased heat flux from exposed human skin to 156 W m −2 and was successful at avoiding condensation. While this study was conducted outdoors, this demonstration and evaluation will help inform subsequent applications of the technology, such as augmenting comfort in naturally ventilated indoor environments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".