Scaling of Flexural and Compressive Ice Failure
Bibliographic record
Abstract
Physical model tests are a powerful means of obtaining solutions to a variety of engineering problems. The applications in hydraulics and aerospace engineering are prominent, where the use of similitude and dimensionless numbers is well developed. The first step is to understand the mechanics of the process. In the case of ice, the theory has not been developed to the same degree as in fluid mechanics. The use of scale models in test basins has often focused on resistance to ship motion and on flexural failure of the ice. This has been reasonably well addressed. The properties of the model ice have often been modified to permit scaling of flexural strength as well as elastic modulus to achieve appropriate behaviour. Extension of testing to situations where ice fails in compression or combined flexure and crushing leads to additional complication. At low rates of loading, ice creeps and also demonstrates enhanced rates of creep if the stress is sufficient to cause damage (microstructural change) in the ice. At higher rates of loading, fracture processes result in a localization of loading, and in the formation of high-pressure zones, which have their own special failure process. In the paper a review of scaled ice testing is given, with associated mechanics including flexural failure. This is followed by a discussion of the failure processes in compression and related mechanics such as creep, damage and fracture. Suggestions as to scaling of these processes are made. An important aspect that is considered is the randomness of ice loads as measured in the full scale. Modelling this aspect and determination of appropriate extreme values is discussed. The Weibull modulus is suggested as an appropriate parameter.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".