Simulating the Swissair flight 111 in-flight fire using the CFD fire simulation software SMARTFIRE
Bibliographic record
Abstract
At 8.18pm on 2 September 1998, Swissair Flight 111 (SR 111), took off from New York’s JFK airport bound for Geneva, Switzerland. Tragically, the MD-11 aircraft never arrived. According to the crash investigation report, published on 27 March 2003, electrical arcing in the ceiling void cabling was the most likely cause of the fire that brought down the aircraft. No one on board was aware of the disaster unfolding in the ceiling of the aircraft and, when a strange odour entered the cockpit, the pilots thought it was a problem with the air-conditioning system. Twenty minutes later, Swissair Flight 111 plunged into the Atlantic Ocean five nautical miles southwest of Peggy’s Cove, Nova Scotia, with the loss of all 229 lives on board. \n \nIn this paper, the Computational Fluid Dynamics (CFD) analysis of the in-flight fire that brought down SR 111 is described. Reconstruction of the wreckage disclosed that the fire pattern was extensive and complex in nature. The fire damage created significant challenges to identify the origin of the fire and to appropriately explain the heat damage observed. The SMARTFIRE CFD software was used to predict the “possible” behaviour of airflow as well as the spread of fire and smoke within SR 111. 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. Possible fire and smoke spread scenarios were studied to see what the associated outcomes would be. This assisted investigators at Transportation Safety Board (TSB) of Canada, Fire & Explosion Group in assessing fire dynamics for cause and origin determination.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".