Multi-Particle Modelling of Compressive Ice Failure During Indentation by Rock Particles
Bibliographic record
Abstract
Abstract The indentation of rock particles into ice is an important aspect of understanding subsea interactions involving ice features. If rock particles are present at the interface between ice and an engineered structure, the question arises: will localization of contact by the rocks cause the ice to fail more easily, resulting in reduced ice pressures, or will rocks transmit high ice pressures through smaller contact areas between the rock and structure resulting in more intense contact stresses on the surface of the structure? This paper presents initial results from a series of tests investigating the indentation of multiple rock particles into an ice specimen, which builds on earlier ice indentation tests on single unconstrained rock particles. Observations from tests conducted using 170g samples of rocks ranging in size from 9.5mm–19.1mm for tests completed at an indentation rate of 0.5 mm/s for indentation depths of 5 mm and 7 mm are presented and discussed. In addition, preliminary results are presented from a Matlab model that has been developed based on the aggregation of independent, individual particle-ice interaction events to simulate multiple particle-ice interactions. A comparison of experimental and simulated results indicates good general agreement and supports the assumption of independent particle-ice indentation events for the interaction conditions considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".