Modelling Iceberg-Topsides Impacts Using High Resolution Iceberg Profiles
Bibliographic record
Abstract
Abstract Platforms operating in arctic and subarctic regions such as the Grand Banks, Labrador Sea, Barents Sea and offshore Greenland are exposed to the risk of iceberg impacts. These structures must be designed to withstand the impact from an iceberg or be designed to disconnect and move offsite to avoid the impact. Offshore Newfoundland, gravity based structures (GBS) such as the Hibernia and Hebron platforms are designed to withstand an impact from an iceberg. However, current accepted practice is not to design the topsides for impact, but to reduce impact risk to an acceptable level by varying the facility geometry (i.e., topsides elevation or footprint). An analytical model was developed to estimate the frequency of icebergs impacting the topsides using three dimensional (3D) models of the platform and the icebergs. Random shapes and sizes are simulated for each iceberg and 3D shapes are generated using a database of measured 2D iceberg profiles. The iceberg shapes are placed randomly in close proximity to the structure and are set to drift towards the structure in a straight line. The initial point of contact between the iceberg and the structure is determined. Crushing of the iceberg against the platform caisson is considered. The process is repeated a large number of times and the total number of contacts with the topsides are determined. In 2012, Hibernia Management and Development Company Ltd. (HMDC) sponsored a field program in which high resolution iceberg profile data were collected. The high resolution iceberg profiles contain detailed 3D information of the above water and below water shape of the iceberg. This paper describes updates to the existing-topsides impact model to take full advantage of the detailed 3D iceberg profiles. These updates include new iceberg shape databases for simulation, and the addition of a detailed iceberg management model and a graphical user interface (GUI) to improve the functionality of the software.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".