Physical Model Testing for Supporting Ice Force Model Development of DP Vessels in Managed Ice
Bibliographic record
Abstract
Abstract The stationkeeping performance prediction of a Dynamic Positioning (DP) vessel greatly depends on the accurate modelling of the ice forces, which in turn depends on managed ice field characteristics (ice concentration, floe thickness, floe size, ice drift speed and direction and inclusion of brash ice and small ice pieces) and the DP system characteristics (DP gain set-ups, control algorithms etc.). Physical model testing is a key tool in understanding and validating the fundamental relationships between the ice environmental parameters and the dynamics of a DP vessel. The National Research Council's Ocean Coastal and River Engineering Research Centre (NRC-OCRE) has conducted two comprehensive series of experiments with one 1/40 scaled and one 1/19 scaled DP vessels, in various realistic managed ice conditions in the ice tank facility in early 2015 and in early 2018, respectively. The primary objective of the model testing programs was to generate a database on managed ice-DP vessel interactions, which was the core to NRC-OCRE's ice force model development and validation activities. This paper describes the model test planning, preparation of managed ice field, the procedure of the model tests and the methodologies of data analysis for the two model testing programs. In both programs, the physical and mechanical characteristics of the ice field were modelled by controlling ice concentration, ice thickness, floe size, ice strength and the ice drift speed and direction. The ice concentration ranged from a light condition (7/10th) to a very heavy condition (9/10th+) with multiple ice floe sizes ranging between 12.5m to 100m. Multiple ice thicknesses ranging between 0.4m to 2m were used for multiple ice drift speeds (0.2 knots, 0.5 knots, and 1.2 knots) with various moderate to extreme ice encroachment angles. Ice forces were not measured directly but estimated based on the thrusters’ response. In addition, model's 6-DOF motions and accelerations were recorded. Multiple high definition cameras were used to capture the global and local ice-structure interactions both placed in above water and underwater locations. For the 2018 testing program, a new ceiling based video system was introduced that captured the images of the ice basin at multiple overlapping locations, which were processed offline to obtain time sequence full image of the ice basin. Model testing results for a few representative cases are presented in this article. The DP system used in the testing demonstrated capabilities of the vessel in maintaining station for majority of test cases. The measurements as well as the videos showed complex and highly stochastic ice-ship-boundary wall interactions, particularly for high oblique cases. The data and video captured provided sufficient information for developing novel ice force models for real time applications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".