A physically-based formulation for texture evolution during dynamic recrystallization. A case study for ice
Bibliographic record
Abstract
Dynamic recrystallization can have a strong impact on texture development during the deformation of polycrystalline materials at high temperature, in particular for those with strong viscoplastic anisotropy such as ice. Owing to this anisotropy, recrystallization is essential for ensuring strain compatibility. The development of recrystallization textures leads to significant mechanical softening, both in laboratory or natural conditions (glaciers, ice sheets). Accurately predicting ice texture evolution due to recrystallization during tertiary creep remains a challenge, yet is crucial to account adequately for texture-induced anisotropy in large-scale models of glacial ice flow. We propose a new formulation for texture evolution due to dynamic recrystallization. This formulation is physically-based on an orientation attractor which maximizes the Resolved Shear Stress (RSS) on the easiest slip system in the crystal (basal slip for ice). The attractor is implemented in an equation of evolution of the crystal orientation with deformation, which is coupled to an anisotropic viscoplastic law (Continuous Transverse Isotropic - CTI) that provides the mechanical response of the ice crystal. The set of equations, which is the core of the R3iCe open source model is solved using finite elements method with a semi implicit scheme coded using the Rheolef library. R3iCe is validated by comparison with laboratory creep data for ice polycrystals under simple shear, uniaxial compression and tension. It correctly reproduces the texture evolution and the mechanical softening observed during tertiary creep. R3iCe therefore allows predicting enhancement factors that may be implemented in large-scale flow models. Although the validation was performed for ice, the R3iCe implementation is generic and applies for any material adequately described using a CTI law.
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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 | 0.000 | 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) | 0.000 | 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".