Quantitative review of experimental tests and theoretical models of flashover occurrence in compartment fires
Bibliographic record
Abstract
: In performance-based fire design (PBFD), flashover is the rapid transition from a growing to a fully developed fire. In this study, a comprehensive literature review and analysis of 93 large-scale compartment fire experiments were conducted to identify the key factors that affect the HRR required for flashover (Q FO ). For each fire test, key parameters were documented, including fuel load, fuel type, compartment configuration, ventilation properties, boundary characteristics, and the heat release rate (HRR)-time curve. The impact of each parameter on Q FO was assessed through comparison with experimental data. It was shown that there are direct correlations between these parameters and Q FO . Moreover, available analytical models to predict Q FO were compared against the compiled experimental results. Based on experimental data, an equation was proposed to estimate Q FO by considering the effect of fuel load, opening factor, boundary characteristics, and compartment shape. These parameters, not previously used all together in other models, resulted in improved accuracy, with the proposed model achieving a mean squared error (MSE) of 0.46 and an R 2 value of 86%, outperforming other theoretical models. The average time to flashover onset, calculated using the proposed equation based on 8,800 different scenarios of the same compartment as a case study, varies from 1 minute for an ultra-fast fire to 11 minutes for a slow-growing fire, indicating the need for a fire safety strategy that accounts for different parameters influencing flashover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".