Estimating Characteristics of Industrial BLEVEs and VCEs from Observed Condensation Clouds
Bibliographic record
Abstract
It is known that the passage of a shock wave in a moist atmosphere can produce a condensation cloud that is briefly visible to the human eye. Recent accidents (e.g. Toronto August 2008) involving boiling liquid expanding vapour explosions (BLEVE) and vapour cloud explosions (VCE) have shown such condensation clouds.In this age where video footage of an explosion incident is the norm (i.e. from the smart phone of a remote observer, or from a security video camera) it is very likely that there will be visual evidence of explosions. This evidence may include the size of a condensation cloud from a shock wave. This paper presents an analysis that allows us to estimate the overpressure of the shock wave at the edge of this condensation cloud. In many cases we can also determine the distance to this shock overpressure from the video image. With this overpressure and distance data it is possible to estimate the energy of the explosion and the overpressure and expected damage at other distances. This could be very useful for accident analysis. Limited video footage of BLEVE tests is used to provide and limited validation the method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".