Exploring membrane-assisted radiant cooling for designing comfortable naturally ventilated spaces in the tropics
Bibliographic record
Abstract
This research proposes the use of membrane-assisted radiant panels to improve the thermal comfort of naturally ventilated spaces in hot and humid climates. These radiant panels are capable of conditioning naturally ventilated spaces, which is impractical with conventional mechanical cooling systems. For conventional systems, a permeable envelope will result in energy wastage from conditioned air escaping or condensation occurring on the radiant surfaces. In our system, there is no air-conditioning and we avoid condensation by separating the radiant surfaces from humid air using a membrane transparent to thermal radiation. The membrane-assisted radiant panels are an unutilized technology for architects to design comfortable naturally ventilated spaces. We propose a cooling system based on the technology and discuss the architectural implications, particularly the permeability of the building envelope and requirements for mechanical spaces, of employing this system in a case study that is a naturally ventilated classroom. Our system is compared to conventional cooling systems. Although our system requires a ceiling space reconfiguration, it does not require duct works and envelope retrofits. The comparative case study shows a potential 52% reduction in cooling energy demand from initial estimation. Considering the trade-offs, our system can be a good alternative for retrofit projects.
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 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".