Arctic LNG carrier structural risk analysis for iceberg collisions
Bibliographic record
Abstract
Structural risk analysis for a new 172,600 m3 Arctic LNG Carrier was carried out. Finite element software, LS-DYNA, was used for the analysis. Three different iceberg masses were used: 3,320 ton, 6,640 ton and 10,000 ton. Six impact simulations were conducted for the condition where no water was present where the vessel forward speed was ~19.5 kt and the iceberg speed was 5 kt perpendicular to the tracking line of the ship. Impacts were targeted on specific areas of the vessel’s bow section. Bell-shaped icebergs, specified by DSME, and more realistic vase-shaped icebergs, developed by NRC, were used for the simulations. The maximum contact force that was measured was in the 80 - 90 MN range for the 10,000 ton iceberg for either shape. The maximum deflections of the outer and inner hulls for these cases were -263.9 mm and -29.5 mm respectively. Two simulations using the 10,000 ton NRC vase-shaped iceberg and DSME bell-shaped iceberg were conducted where water, and associated hydrodynamics, was included. For these wet-case simulations the vessel speed was ~19.5 kt and the maximum impact force was in the same approximate range as the dry-case simulations. The outer and inner hull deflections for the wet-case simulations were significantly higher than those for the dry case because the deformable hull section was less constrained and consequently more flexible than the actual case corresponding to the dry-case simulations. Ice contact areas and average pressures were determined for seven cases. All of the simulations generated sliding-load impacts. No rupturing/tearing of the outer hull was observed for any case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".