Bonded discrete element method for modeling level ice
Bibliographic record
Abstract
This paper investigates a numerical method for ship-ice interactions which is vital for safe and efficient maritime operations in polar environments. The study focuses on the bonded discrete element method (DEM) to simulate a large composite ice piece and its failure processes. To validate this approach, a series of tests using a cantilever beam are performed and compared with typical values measured from NRC’s model ices, with a particular focus on flexural strength. Within this validation process, a parametric study is conducted to assess the impact of initial particle arrangements, including the number of layers (or diameter of baseline spherical particles), the elastic modulus of bonds, and tensile failure criteria. An optimal configuration is proposed for realistic ice simulation while maintaining computational efficiency. Using the proposed modeling parameters, an ice plate penetration scenario involving an ascending vertical cylinder is simulated. The estimated ice loads and crack propagation patterns are compared with experimental data, illustrating that the bonded DEM method effectively captures the maximum contact forces. Furthermore, the proposed setups can be extended to simulate similar ice conditions, such as varying ice thicknesses.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".