Improved Added Mass Modeling for Ship-Ice Interactions Based on Numerical Results and Analytical Models
Bibliographic record
Abstract
Abstract Global ice loads at different locations on a ship during rams into ice are a function of ship motions and added mass in addition to the failure mode and strength of the ice. In the literature, various analytical added mass models have been used for ship-ice interactions, which could lead to significant differences in the prediction of global ice loads on ships. In this work, an improved added mass model has been developed based on numerical results and existing analytical models. Added mass coefficients of three ice-going ships, CCGS Amundsen, CCGS Louis S. St-Laurent and MVArctic, were estimated using four analytical added mass models. It was found that the differences in added mass coefficients predicted by these models are significant and enhancements can be made. A body-exact numerical simulation tool based on the potential-flow theory, MAPS0, has been used to compute the added mass coefficients of the three vessels in the frequency domain and the results were used for the development of the improved added mass model. The improvement in the load predictions has been demonstrated by applying the new added mass model to the three vessels.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".