Simulations of Ice Rubbling against Conical Structures Using 3D DEM
Bibliographic record
Abstract
Understanding ice rubble build-up is important in designing structures such as offshore platforms, bridge supports, and breakwaters for use in arctic and cold regions. Past numerical investigations to understand rubble pile formation and ice loads against slopes in two dimensions indicate that ice thickness and structure slope angle are dominant parameters. This work uses a three-dimensional discrete element method (3D DEM) bonded particle model to simulate ice interacting with an upward-sloping cone. As with past 2D work on slopes, this investigation considered ice thickness and slope angle, but also considered block/particle size and sheet composition. Rubble pile characteristics of interest included height, shape, volume, and formation mechanisms (such as sliding, rotation, and collapse). In extending the 2D slope to a 3D cone, the geometry of the Confederation Bridge across Canada’s Northumberland Strait was used a starting point. This paper focuses on qualitative observations and learnings arising from the 3D simulations. These insights contribute to our current understanding of ice interaction with cones and serve to guide others wishing to undertake similar 3D DEM research into ice. The paper concludes with a discussion of potential future extensions, such as the use of finely-tuned DEM models and parameters to more accurately estimate ice loads against conical structures, and the repetition of similar numerical experiments to include ridges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".