Comparison of Analytical and Numerical Models of Glancing Ship-Ice Collisions
Bibliographic record
Abstract
The International Association of Classification Societies (IACS) Polar Class (PC) requirements are widely accepted by the industry as the design standards for vessels operating in polar regions. The PC requirements consider the bow shoulder collision with infinite ice as the base scenario. The Popov collision model (Popov, 1976; Daley, 1990), updated with a pressure-area ice pressure model, was adopted for calculating the ice loads on the hull. The Popov collision model considers that the ship-ice impact is so quick that a 3-D collision can be modeled by an equivalent 1-D collision. All motions between ship and ice are mapped onto the normal direction to the hull at the collision point. No collision energy is assumed to be dissipated by the friction force since sliding velocities are ignored. It is of interest to assess the effects of the simplifications in the Popov model using a numerical method which can simulate the collision mechanism with a high level of sophistication. A numerical study of the Popov collision model of ship-ice interaction is presented in this paper. In the study, ship sliding motions and frictions between hull and ice were included in the six-degree-of freedom ship-ice collision model using LS-DYNA software. The output and checked items include the force time histories, maximum force value, ship/ice motions, contact areas and positions, etc. A parametric study was carried out to quantify the effects from varying mesh sizes, ship-ice friction coefficients, ice elastic modulus and ice buoyancy force using the FE model simulation. The effects of simplifications of the Popov analytical model were assessed through the comparisons of the results produced by the analytical model and the numerical model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".