Energy And Fire Safety Performance Of Atrium Ventilation In High-rise Buildings
Bibliographic record
Abstract
Ventilative cooling is an effective approach to remove indoor overheat, thus reducing cooling load and peak electricity demand. In high-rise buildings, the stack effect could contribute to more building ventilation and coolingrelated energy savings. However, it also brings much concern on the fire safety issues, which, therefore, blocks the ventilative cooling application in high-rise buildings due to the limited study on the interaction effects between fire safety and energy efficiency of high-rise ventilation. To fill in this research gap, this paper aims to investigate the impacts of fire safety design, i.e. adding segmentation in the high-rise atrium, on the high-rise ventilative cooling performance. Both fire smoke simulations and building energy simulations were conducted to investigate the impacts of segmentation slab on the performance of fire protection as well as the ventilative cooling. It was found that the segmentation could effectively protect the upper space which is far from the fire source, but it reduces the energy savings of ventilative cooling due to the higher flow resistance. Therefore, it is quite necessary to evaluate both of fire protection performance and energy efficiency for high-rise ventilation design.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".