The Dynamic Failure Behaviour of High-Pressure Zones during Medium-Scale Ice Indentation Tests
Bibliographic record
Abstract
Results from medium-scale ice-crushing dynamic tests are presented in this paper based on a series of indentation experiments on confined ice samples using spherical indenters to simulate high-pressure zones (hpzs) with areas on the order of 103–104 mm2. The effects of ice temperature, interaction speed, indenter size and structural compliance on failure behaviour and associated structural dynamics have been studied. Observed failure behaviour consisted of a combination of continuous crushing extrusion and intermittent spalling, both of which were highly dependent on test conditions. Overall, the effects of the studied conditions on ice failure behaviour and associated interaction dynamics were found to be similar to the results reported from previous small-scale experiments, suggesting scale independence of the mechanisms that dominate ice failure behaviour. In general, warmer ice and smaller contact areas are associated with continuous extrusion with intermittent spalling, resulting in smoother peak pressures, while colder ice and larger contact areas tend to result in fracture-dominated behaviour with sharp peaks and substantial load drops. Ice temperature was also found to significantly influence interaction dynamics, with colder ice showing larger amplitude and longer duration dynamic activity, and higher peak pressures. Interaction speed was observed to primarily affect dynamic aspects of ice–structure interactions, with faster tests leading to higher failure frequencies. Similarly, structural compliance was found to mainly impact failure frequency, as well as the extent of load drops, with compliant structures tending to produce more significant load drops following failure. Overall, these experiments have helped enhance our understanding of compressive ice failure and contribute to improved models for dynamic ice–structure interactions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".