Teardrop and parabolic lens yield curves for viscous-plastic sea ice models: New constitutive equations and failure angles
Bibliographic record
Abstract
Most viscous-plastic sea ice models use the elliptical yield curve. This yield curve has a fundamental flaw: it excludes acute angles between deformation features at high resolution. Conceptually, the teardrop and parabolic lens yield curves offer an attractive alternative. These yield curves feature a non-symmetrical shape, a Coulombic behavior for the low-medium compressive stress, and a continuous transition to the ridging-dominant mode. We show that the current formulation of the teardrop and parabolic lens viscous-plastic yield curves with normal flow rules results in negative or zero bulk and shear viscosities and, consequently, poor numerical convergence and representation of stress states on or within the yield curve. These issues are mainly linked to the assumption that the constitutive equation applicable to the elliptical yield curve also applies to non-symmetrical yield curves and yield curves with tensile strength. We present a new constitutive relation for the teardrop and parabolic lens yield curves that solves the numerical convergence issues naturally. Results from simple uni-axial loading experiments show that we can reduce the residual norm of the numerical solver with a smaller number of total solver iterations, resulting in significant improvements in numerical efficiency and representation of the stress and deformation field. These yield curves lead to smaller angles of failure, in agreement with theoretical predictions, and are good candidates to replace the elliptical yield curve in high-resolution pan-arctic sea ice simulation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".