Experimental and numerical study of smoke conditions in an atrium with mechanical exhaust
Bibliographic record
Abstract
Fullwscale experiments and numerical modelling using Computational Fluid Dynamics techniques were employed to investigate atrium smoke exhaust in physical model studies. These investigations are part of a joint research project between the American Society of Heating Refrigerating and Air-Conditioning Engineers Inc. (ASHRAE) and the National Research Council of Canada (NRCC). The objective of these studies is to develop input to design guides for atrium smoke management systems. This paper presents initial results from this study. The physical tests were done in a specifically constructed compartment equipped with a smoke exhaust system and instrumentation for measuring temperatures, CO2 concentrations and velocities. Fire was modelled using propane burners capable of producing fires with different intensities and areas. The numerical simulations were done using Computational Fluid Dynamics (CFD) models. A comparison between the experimental and predicted temperatures and CO2 concentrations indicated that the CFD model can predict the conditions in the room. as well as the depth of the hot layer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".