CFD fire simulation of the Swissair flight 111 in-flight fire – Part 1: Prediction of the pre-fire air flow within the cockpit and surrounding areas
Bibliographic record
Abstract
The SMARTFIRE computational fluid dynamics (CFD) software was used to predict the ‘possible’ behaviour of airflow as well as the spread of fire and smoke within a Swissair configured McDonnell Douglas MD-11 commercial transport aircraft. This work was undertaken by the Fire Safety Engineering Group (FSEG) of the University of Greenwich as part of Transportation Safety Board (TSB) of Canada, Fire & Explosion Group’s investigation into the in-flight fire occurrence onboard Swissair Flight 111 (SR111): TSB Report Number A98H0003. The main aims of the CFD analysis were to develop a better understanding of the possible effects, or lack thereof, of numerous variables relating to the in-flight fire. This assisted investigators in assessing possible fire dynamics for cause and origin determination. In Part 1, the numerical analyses to pre-fire airflow patterns within the cockpit and its vicinity are presented. The pre-fire simulations serve two ends. One is to provide insight into the flow patterns within the cockpit and its vicinity and further supportive numerical evidence for the airflow flight test observations. The other is to provide plausible initial flow conditions for fire simulations. In this paper, some flow patterns at a number of primary locations within the cockpit and its vicinity are highlighted and the predicted flow patterns are compared with the findings from the airflow flight tests. The predicted patterns are found to be in good qualitative agreement with the experimental test findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".