Guidance and control of iceberg towing operation in open water, with experimental testing
Bibliographic record
Abstract
Icebergs pose serious threats to existing and planned offshore structures, vessels, and operations in Arctic waters such as the East Coast of Canada, East and West Greenland, the Barents Sea, and the Kara Sea. A collision between an offshore installation and an iceberg could cause serious damage to the installation, and in a worst case scenario take life. Therefore, if an iceberg is evaluated as a threat, physical iceberg management must be mobilized to mitigate the threat. For open water, this is typically done by single vessel towing of the iceberg using steel hawser and synthetic floating tow lines.This work describes a model for open water iceberg towing using a single towing vessel. This includes a mathematical model of the towing vessel, the iceberg and the towline between them. It also looks into certain towline configuration choices, estimation of damping and mass, and other things that can affect the towing model. The mathematical model was based on the work of [marchenko2008] and then generalized to the Fossen-style of notation [fossen2011].A maneuvering controller was designed for use in the towing operation. The controller was designed using maneuvering theory as described by [skjetne2005]. The controller is responsible for guiding the ship along a path, with the iceberg trailing behind it. Another controller has been designed for controlling the tension in the towline. In addition to the controllers, several observers had to be designed. These observers are responsible for estimating position, velocity, bias, and tension in the system.Finally, an experiment with the CS Enterprise I model vessel, and an emulated iceberg, was conducted in a towing tank. The experiment gave important qualitative data regarding the iceberg towing system, and confirmed that the controller worked in a real-life scenario.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".