Modeling and risk analysis of large‐scale crude oil pool fire on an offshore facility
Bibliographic record
Abstract
Abstract Large‐scale pool fires on offshore platforms can have disastrous consequences. Wind load is an essential component of the marine environment; hence, wind‐shielding facilities are installed on the platforms as needed. A computational fluid dynamics technique is used in this study to model large‐scale pool fires. The effects of wind load and windshields on large‐scale pool fires are investigated. Furthermore, the impact of the large‐scale pool fire on personnel is evaluated based on thermal radiation dose. It has been discovered that the growing horizontal momentum of the wind load might result in a significant distance of fatal injury. The windshields have double‐edged effects, including that: (1) they can reduce the injury distance by preventing the spread of fire products; and (2) they could block the release of heat and smoke. It is advised that while installing windshields on offshore platforms, different fire scenarios need to be considered. This research can be used to provide technical support for the development of the emergency evacuation strategy and layout design of offshore platforms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".