Cool running: Passive radiative cooling to sub-ambient temperatures inside naturally ventilated buildings
Bibliographic record
Abstract
One priority for avoiding runaway climate change is finding viable alternatives to mechanical air-conditioning.Recent advances in daytime radiative cooling materials are promising.However, researchers have not yet shown how to use them for passive cooling below ambient temperature indoors.Ventilation is challenging in this regard as healthy air changes will heat the sub-ambient interior.We present a field study using analog models to observe how daytime radiative cooling materials can passively reject heat from inside naturally ventilated buildings.We mounted two test boxes on a rooftop in Southern California, replicating in miniature the thermal loads, losses, and air changes for one occupant.The control box represented a reference 'gold standard' for passive cooling: internal thermal mass with night ventilation.The test box had an uninsulated metal roof with a top surface for daytime radiative cooling.Both boxes had internal heat sources with ventilation driven not by wind but by buoyancy.Under clear skies, the test box maintained an interior temperature of 3.9 +/-4.8 °C below the mean prevailing exterior temperature while venting 6.9 +/-0.3 air changes per hour during the day.In comparison, the control box maintained an interior temperature of 5.0 +/-1.7 °C above the mean prevailing exterior temperature while venting 8.6 +/-0.1 air changes per hour during the night.We show with a calibrated model how to improve the sub-ambient temperature stability of the test box with more thermal mass in the roof and how to scale up the results to actual buildings. Significance Statement.In a warming world, new passive cooling techniques could help curtail the growing demand for mechanical air-conditioning and the resulting emissions that further heat the planet.Recent breakthroughs in passive radiative cooling materials, which reflect sunshine while emitting infrared heat into cold outer space, show promise.But it's unclear how these materials could replace mechanical heat rejection in well-ventilated buildings.In a field experiment with two model buildings, each scaled to one person's heat load and fresh air needs, we show how to chill the air below the ambient temperature inside a naturally ventilated enclosure by coupling terrestrial radiation, thermal mass, and gravity-driven air changes, while outperforming a reference 'gold standard' for passive cooling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".