Landfast sea ice material properties derived from ice bridge simulations using the Maxwell elasto-brittle rheology
Bibliographic record
Abstract
Abstract. The Maxwell elasto-brittle (MEB) rheology is implemented in the Eulerian finite-difference (FD) modeling framework commonly used in classical viscous-plastic (VP) models. The role of the damage parameterization, the cornerstone of the MEB rheology, in the formation and collapse of ice arches and ice bridges in a narrow channel is investigated. Ice bridge simulations are compared with observations to derive constraints on the mechanical properties of landfast sea ice. Results show that the overall dynamical behavior documented in previous MEB models is reproduced in the FD implementation, such as the localization of the damage in space and time and the propagation of ice fractures in space at very short timescales. In the simulations, an ice arch is easily formed downstream of the channel, sustaining an ice bridge upstream. The ice bridge collapses under a critical surface forcing that depends on the material cohesion. Typical ice arch conditions observed in the Arctic are best simulated using a material cohesion in the range of 5–10 kN m−2. Upstream of the channel, fracture lines along which convergence (ridging) takes place are oriented at an angle that depends on the angle of internal friction. Their orientation, however, deviates from the Mohr–Coulomb theory. The damage parameterization is found to cause instabilities at large compressive stresses, which prevents the production of longer-term simulations required for the formation of stable ice arches upstream of the channel between these lines of fracture. Based on these results, we propose that the stress correction scheme used in the damage parameterization be modified to remove numerical instabilities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 teacher head, 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".