Optimization research on smoke prevention and exhaust system for air-bearing membrane structure buildings
Bibliographic record
Abstract
Due to its advantages in energy conservation, emission reduction and recyclability, air-supported membrane structure (ASMS) has been widely implemented in various applications, including large stadiums, conference centers, warehouses and temporary buildings.Compared to traditional building, the structural and material properties of ASMS are special which significantly affect smoke diffusion and flow behavior during fire scenarios.Moreover, the design methods for smoke management system of ASMS buildings are still lacked and further research is required.In this study, a numerical model of a coal storage bin was built.The impact of several key design parameters, including such as height of natural smoke vent, fire source location and makeup air methods, on the performance of ASMS building`s smoke management system were evaluated.Gas temperature, CO concentration, visibility, smoke exhausting quantity, critical pyrolysis temperature and critical heat radiation intensity were selected as evaluation indexes.The results indicated that better natural smoke exhaust efficiency was achieved when the natural smoke vents were located at the height between 80% and 100% of the building's total height.Setting natural makeup air vent in the wall of smoke bay 3 can achieve the best cooling effect.However, compared to natural makeup air, the natural smoke exhaust efficiency of mechanical makeup air was much better.When the rate of mechanical makeup air reached 120% of the required natural smoke exhausting quantity, it has significant improvements in temperature reduction, visibility and natural smoke exhausting quantity which are crucial for ensuring safe evacuation during fire events.These findings provide a design refer for enhancing fire protection and construction practices in ASMS buildings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".