An Assessment Capability for LNG Leaks in Complex Environments
Bibliographic record
Abstract
Pollutants may be introduced into urban or marine settings by various means and could result in an adverse impact to public safety and the environment. Therefore, it is important for emergency management personnel to understand the potential risks and physical extents of a leaked substance, whether it is toxic, flammable or explosive. Traditional tools for predicting the atmospheric dispersion of leaked substances are quick and simple to use, but may not adequately consider the effects of the built environment that includes complex urban and terrain geometries. Alternatively, CFD methods have been increasing in application; although, their superior accuracy is met with commensurate manual effort. The All Hazards Planner is a fast, accurate gas dispersion modelling tool for city and port environments, which employs a full-physics CFD approach but automates the intensive manual effort. In this work, a credible LNG leak from a 12-mm-diameter hole is modelled for two hypothetical case studies: adjacent to an LNG tanker and between a cruise ship and pier during bunkering. The LNG vapour flammability extents are compared to an empirical model in the absence of geometry effects and are contrasted with geometry effects to highlight the importance of the real environment. The free-field extents are invariant, whereas the inclusion of geometry is shown to reduce the flammability extents by spreading at the ground-level and forcing the plume upwards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".