A model for estimating the probability of missile impact: Missiles originating from bursting horizontal cylindrical vessels
Bibliographic record
Abstract
Abstract Past explosion events in process facilities have centered attention on the fact that missiles generated as the result of vessel fragmentation pose significant risk to personnel and process equipment and can trigger knock‐on or domino effects in industrial accidents. To promote the design of inherently safer facilities—and to enable more effective mitigation and control measures with respect to missile risks—it is necessary to perform missile risk analysis studies at the early design phase. To aid in such an analysis and to predict domino scenarios, it is essential to have models that can quantify (1) the probability of a missile impact on a target and (2) the consequences of the probable impact. In the present work, we propose a model to quantify the probability of missiles that originate from bursting horizontal cylinders, having an impact on spherical target vessels located in a process area. Although previous investigations on the quantification of missile impact probability were based on a single‐scenario approach, the current model is based on two credible scenarios. The model is built on the concept of a vulnerable area (VA), defined as the probable impact zone sketched around the target object. The concept of an effective range interval (ERI) was adopted and was extended by introducing an effective trajectory interval (ETI) and effective orientation interval (EOI). The uncertainties in the model parameters were addressed by means of Monte Carlo sampling approach. A case study is used to evaluate the performance of the proposed model. The case study uses a grid‐based approach (GBA) to provide interactive contour plotting of the missile impact probabilities over a defined process area. © 2006 American Institute of Chemical Engineers Process Saf Prog, 2007
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".