Influence of Wave-Induced Iceberg Heave Motions on Risks to Subsea Equipment
Bibliographic record
Abstract
Abstract In offshore regions where risk of iceberg contact with subsea equipment is unacceptably high, protection options include reduction of the probability of impact by placing the equipment below the seabed in an excavation, and use of protective structures to reduce the probability of damage given an impact. An initial estimate of the probability of impact from a freely drifting or scouring iceberg may be determined based on the chance that an iceberg with draft greater than the top of the subsea equipment drifts directly over it. A more detailed analysis might consider the increase in probability of impact as a result of iceberg heave and pitch motions. In this paper, a methodology is presented for assessing the importance of heave motions of freely floating icebergs on probability of impact with subsea equipment protruding above the seabed. The method incorporates numerical and statistical modeling of random icebergs motions in irregular seas, including shallow water effects. Consideration is given to the mean clearance of the iceberg above the equipment, the random iceberg wave-induced heave motions, the geometries of the structure and iceberg and the time for the iceberg to pass over the site. Analyses are conducted on the effect of wave-induced heave motions of icebergs in wind-generated sea states and swell in water depths of 95 m and 60 m. A number of iceberg shapes are considered. It is found that for the conditions considered, heave motions do not have a significant influence on impact probability. In evaluating the previous methods for estimating wave-induced iceberg motions, some deficiencies in previous iceberg heave and surge response amplitude operators were discovered, and improvements are recommended when estimating iceberg impact loads with gravity based structures and floating systems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".