Dynamic vulnerability assessment of process plants with respect to vapor cloud explosions
Bibliographic record
Abstract
Vapor cloud explosion (VCE) accidents in recent years such as the Buncefield accident in 2005 indicate that VCEs in process plants may lead to unpredicted overpressures, resulting in catastrophic disasters. Although a lot of attempts have been done to assess VCEs in process plants, little attention has been paid to the spatial-temporal evolution of VCEs. This study, therefore, aims to develop a dynamic methodology based on discrete dynamic event tree to assess the likelihood of VCEs and the vulnerability of installations. The developed methodology consists of six steps: (i) identification of hazardous installations and potential loss of containment (LOC), (ii) analysis of vapor cloud dispersion, (iii) identification and characterization of ignition sources, (iv) explosion frequency and delayed time assessment using the dynamic event tree, (v) overpressure calculation by the Multi-Energy method and (vi) damage assessment based on probit models. This methodology considers the time dependencies in vapor cloud dispersion and in the uncertainty of delayed ignitions. Application of the methodology to a case study shows that the methodology can reflect the characteristics of large VCEs and avoid underestimating the consequences. Besides, this study indicates that ignition control may be regarded as a delay measure, effective emergency actions are needed for preventing VCEs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".