Numerical Research on Global Ice Loads of Maneuvering Captive Motion in Level Ice
Bibliographic record
Abstract
In level ice, the maneuvering motion of icebreakers has a major influence on the global ice loads of the hull. This study researched the influences of the drift angle and turning radius on the ice loads of the icebreaker Xue Long through a partial numerical method based on the linear superposition theory of ice loads. First, with reference to the Araon model tests performed by the Korea Research Institute of Ships and Ocean Engineering (KRISO), numerical simulations of Araon’s direct motion were carried out at different speeds, and the average deviation between numerical results and model test results was about 13.8%. Meanwhile, the icebreaking process and modes were analyzed and discussed, compared with a model test and a full-scale ship trial. Next, the maneuvering captive motions of oblique and constant radius were simulated to study the characteristics of ice loads under different drift angles and turning radii. Compared with the maneuvering motion model tests in the ice tank of Tianjin University and the Institute for Ocean Technology of the National Research Council of Canada (NRC/IOT), the numerical results had good agreement with the model test results in terms of the variation trend of ice loads and ice–hull interaction, and the influences of drift angle and turning radius on ice resistance and transverse force, which have a certain reference value for sailing performance research and the design of the hull form of icebreaker ships, are discussed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".