Efficient Modelling of Harsh Environment Disturbances for DP and Autonomous Ships Simulations
Bibliographic record
Abstract
Abstract Numerical modelling of the Arctic ocean dynamics with real-time simulation capability is useful for designing, developing, testing, and validating the performance of Dynamically Positioned (DP) and Autonomous ships/offshore platforms. Advanced simulation technology needs to be developed to predict the expected loads on these systems due to the complex interactions with environmental disturbances. This paper presents models of waves, currents, wind, and ice that comply with the real-time simulation requirements and adequately capture the dynamic characteristics of the most relevant physical processes. A 3D dispersive numerical model is deployed to predict the wave parameters to be utilized to compute the wave loads on a ship with known Response Amplitude Operators (RAO). A uniform current load is then incorporated in a superposition manner by using a combined wave-current field dispersion relation capable of expressing the wavenumber of an interactive wave-current field. The mean and the gust wind components are added to the resultant force components. A multiple regression-based ice model is used to predict the loads caused by an ice field characterized by varied ice thickness, concentration, floe size, drift speed and directions. The stationkeeping performance of a generic DP-controlled ship subjected to environmental disturbances is evaluated for a range of environmental conditions. The proposed models can help design, develop, and evaluate dynamic positioning and autonomous ship controllers’ performance. Another application may be developing a realistic simulation environment to train conventional, DP-controlled and autonomous ship operators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".