Qualitative Analysis of Ventilation Position and Dimension Effects on Compartment Fire Dynamics: An Experimental and Numerical Approach
Bibliographic record
Abstract
Abstract Informal settlements, where over 1 billion people live globally, are extremely vulnerable to fire events. Thermally thin steel-clad timber-framed homes found in South African informal settlements are a prime example of this. In this paper, we explore, through six full-scale laboratory experiments and modelling, the influence of opening locations, areas, and aspect ratios, on the fire dynamics of thermally thin and leaky compartments. It was found that having the window on the same wall as the door produced the highest heat fluxes opposite the door (13 kW/m2). Having the window opposite the door on the back wall, created a crossflow scenario which produced slightly higher fluxes opposite the door (10–11 kW/m2) compared to when the windows were on a side wall (7–9 kW/m2). Increasing the opening area by including another equally sized window, or by doubling the window width or height, slightly reduced the heat fluxes opposite the door and window, in general slightly increased the time to flashover, and significantly increased the heat release rate required for flashover. The work presented within this paper adds to the growing body of knowledge around informal settlement dwelling fire dynamics which can be used by engineers and urban planners in understanding and mitigating urban conflagrations within these communities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".