Discrete Element Analysis of Ice Loads on Ships and Structures
Bibliographic record
Abstract
In this paper, the versatility of discrete element analysis (DEM) in modeling ice-related problems is discussed and further demonstrated using the results from the DEM works conducted by the National Research Council’s Institute for Ocean Technology (NRC-IOT) using a commercial code DECICE. These works include a wide range of icestructure and ice-ship interaction problems of current interest, i.e., ice loads on conical structures, jamming of floes at bridge piers, modeling of the mechanical behavior of ice rubble, pack ice stability and associated forces on offshore structures, rubble loads exerted on an inclined retaining wall, ridge keel resistance during seabed scouring, dynamic response of a moored conical drill-ship in ice, and ship maneuvering performance in ice. Representative simulations for each case are presented including load, motion and/or interaction process, whichever is appropriate. The simulations from DECICE were compared with experimental data and found satisfactory in terms of accuracy and real-time simulation capability. The accuracy is important for design and engineering of marine structures and ships, whereas, the real-time simulation capability allows it to be used in marine simulators for personnel training and marine operation assessment. The performance of DECICE is also addressed and improved via the implementation of a new contact detection sequence and parallel considerations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".