Analysis of Medium-Scale Laboratory Tests on Ice Crushing Dynamics
Bibliographic record
Abstract
Abstract The study of ice loads and associated mechanics is highly important in supporting oil and gas exploration and development in ice-prone offshore regions. While knowledge gaps regarding full details of the dynamic ice structure interaction process remain, substantial research is being carried out over a range of scales to improve understanding of excitation mechanisms for structures subjected to compressive ice loading. The present paper is focused on the first of a new series of medium-scale laboratory tests that have been carried out as a part of a larger program of research aimed at improving understanding of compressive ice failure phenomena and links between the formation of high-pressure zones and the occurrence of ice-induced structural vibrations under controlled conditions. The tests presented in this paper focus on the indentation of ice using a single spherical indenter mounted on a compliant beam system. Nine tests were performed to investigate the effect of ice temperature and indenter size on ice failure processes associated with high-pressure zone formation and evolution during dynamic ice crushing tests. Ice failure events were observed from regular and high-speed video synchronized with LVDTs and load cell data. Observations of ice load dynamics and structural response are discussed, along with corresponding observations of failure processes in the ice. In general it was observed that ice at warm temperatures was more prone to ductile type failure with lower, less dynamic pressures. By contrast, results from tests conducted at colder temperatures were characterized by a combination of spalling and crushing failure, which corresponded more with large-amplitude, sawtooth load cycles, which often resulted in load drop to zero as the rebounding structure cleared the failed ice from around the indenter. In terms of scale effects, it was observed for the same indentation rate and temperature, smaller indenters produced higher amplitude, higher frequency sawtooth loading than was observed for larger diameter indenters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".