Transient Computational Fluid Dynamics Analysis of Passive Cooling in a Building with Diurnal Radiative Cooling Material Coated onto Its Rooftop
Bibliographic record
Abstract
Building cooling loads, which continue to increase with increasing global temperatures, are responsible for large quantities of greenhouse gas emissions. Radiative cooling (RC), whereby structures are cooled by emitting radiation in the atmospheric window, from 8–13 μm, to outer space, is a promising clean technology that can be used to meet ever‐increasing building cooling demands. However, the effects of using RC on the airflow velocity and temperature distributions within the occupied zone of buildings are yet to be investigated. Herein, computational fluid dynamics simulations are performed to study the transient airflow velocity and temperature distributions in buildings that are cooled using RC material on their rooftops. For idealized conditions when the thermal mass of the house is neglected, the results show that when the cooling power provided by the RC material is , and the average temperature of the occupied zone in the building is reduced from 295 K to about 293 and 289.7 K after two minutes, respectively. These rapid cooling rates were attained without exceeding head‐to‐ankle temperature differences of about 2.7 °C and with air flow velocities maintained below 19.0 cm s−1, which is consistent with a comfortable environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".