ESTIMATING LNG SPREADING ON WATER1
Bibliographic record
Abstract
In order to meet the growing demands for energy, plans are currently being made to construct several new liquefied natural gas (LNG) off-load terminals in North America. While LNG provides a relatively clean source of fuel, increased shipment means increased potential for accidental releases. Governmental agencies have recognized the safety concerns about such a possible spill event and have recommended the proactive and preemptive addressing of such concerns. Several recent studies have examined the likely thermal and other hazards from an LNG vessel accident. One possible threat from such an incident is a pool fire, which could be quite large since the volume of a single tank on an LNG vessel can contain as much as 25,000 m of liquefied gas. In general, there is agreement in the literature on the pool fire geometry, burn regression rate, and thermal emissive power of the fire but less concurrence on possible tank leak rate or surface spread rate on water. The authors review the existing approaches to this latter phenomenon for unconfined spills. Comparison of model predictions with limited experimental data is also discussed For low wave conditions, two methods, one by Fay and the other by Weber, have been the most widely used in past models, sometimes with modifications that are discussed in the paper. Similarly, two alternative algorithms have recently been suggested for high wave conditions. The authors review the merits of these two approaches, the expected consequences from each approach, and finally compare them to widely used oil spill spreading formulas under energetic wave conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".