Ice Model Tests for Dynamic Positioning Vessel in Managed Ice
Bibliographic record
Abstract
Abstract Stationkeeping in managed ice using dynamic positioning (DP) control system has been an area of great interest over the past few years. The stationkeeping performance of a DP vessel depends on the modelling accuracy of the ice forces, which in turn depends on managed ice field characteristics (floe size, floe thickness, inclusion of brash ice and small ice pieces, ice drift speed and direction) and DP system (gain set-ups). Over the years, many engineers have been using numerical and experimental tools to assess the effect of these parameters. More recently, a comprehensive series of experiments with a 1/40 scaled DP vessel were conducted in various realistic managed ice conditions in the ice tank facility of OCRE-NRC in early 2015. This paper describes the preparation of managed ice field, the procedure of the model tests and the methodologies of data analysis. The physical and mechanical characteristics of the ice field were modelled by controlling ice concentration, ice thickness, floe size, ice strength and ice drift speed/direction. The ice concentration ranged from light condition (6/10th) to very heavy condition (9/10th+) with three different floe sizes (100m, 50m and 25 m). Three different ice thicknesses (0.6m, 1.2m and 2m) were used and three different drift speeds (0.2 kts, 0.5 kts, and 1.2 kts) with various heading angles were tested. Some tests used high strength model ice in order to keep the ice field longer (sometimes for 2nd day). Some tests used properly scaled model ice (700 kPa of flexural strength in full scale) in order to simulate ice failure appropriately. Ice loads were not directly measured but estimated based on the thrusters’ response. Video analysis is introduced and some observations are described. Test results for a few cases are presented as an example.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".