Numerical Modelling of Ice Rubble Interactions Using Discrete Element Method
Bibliographic record
Abstract
Abstract A three-dimensional, freeze-bonded, Discrete Element Method (DEM) numerical model has been developed to simulate various ice rubble/ridge interaction scenarios. The numerical model was validated against the physical tests conducted by C-CORE under the Pipeline Ice Risk Assessment and Mitigation (PIRAM) Joint Industry Project. Accurate representation of ice block geometries and sizes distributions was achieved using clumped particles, rather than the traditional DEM spheres. With the use of clumped ice blocks the numerical model was able to characterize the initial keel conditions (macro porosity and freeze bond contacts) and capture interlocking behavior between ice blocks. A DEM gravel seabed model was then introduced to the clumped ice block model to allow for better representation of soil response during the simulated experiments. The main features of these model developments are described in this paper, along with a comparison of simulated results and large scale physical test results. From this work it was concluded that: (1) clumped ice blocks give more representative ice block shapes for an ice keel than spherical ice blocks, which better capture ice block interactions and overall ridge keel properties and behavior; and (2) a DEM model of the seabed gravel provided a better representation of the seabed than was possible with a continuous stiffness plane, which had important implications for modelling the keel-seabed interactions. The development and inclusion of these two new model features were found to significantly improve the accuracy of the DEM model in reproducing physical test results, while still being sufficiently computationally efficient as to allow for simulation of interactions full-scale ice 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".