Development of a Simulation Performance Laboratory for Assessing DP and MASS Controllers in Complex Wave and Ice Environments
Bibliographic record
Abstract
Abstract Activities like shipping and harvesting ocean energy are increasingly being conducted in harsh, ice-rich waters. Analytical and numerical models, validated against full-scale and model testing results, are vital to developing new technologies and understanding issues posed by operating in such environments. This paper presents the development and initial validations of a simulation performance laboratory, SPLASH, for modelling complex ice, wave and current interactions with dynamic positioning (DP) platforms and marine autonomous surface ships (MASS). SPLASH has a modular architecture, with components with well-defined interfaces tied together by a central controller. The core component is the numerical engine that models the floating system (e.g., a ship), with active propulsion and steering systems, and the complex environments, including current, waves and rigid-body ice pieces. The central controller can interface with external controllers to govern the movement of the floating system. In the current research, two case studies were carried out: the first aimed to model and validate a complex ice-structure interaction scenario involving an ice field comprised of over 75,000 ice pieces, including brash ice, and a DP-controlled ship operating under various configurations; the second focused on modelling wave-ice-DP-ship interaction scenarios, where both regular and irregular wave are modelled in the presence of an ice field with a large number of ice pieces. The preliminary results show promise in offering a platform for accurately and efficiently assessing advanced control systems for DP/MASS operations in complex ice-wave-current environments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".