Thermobarokinetics of ice: constitutive formulation for the coupled effect of temperature, stress, and strain rate in ice
Bibliographic record
Abstract
Abstract. Understanding and modeling the mechanical behavior of ice under varying thermal and loading conditions is essential for cryospheric science, permafrost engineering, and the design of polar infrastructure. A central challenge lies in capturing the strong coupling between stress, strain rate, and temperature, an interdependence referred to in this work as the thermobarokinetics of ice. This study presents a three-dimensional constitutive model that explicitly incorporates this coupling through a unified thermomechanical framework. Notably, the model employs shared functional dependencies for both viscosity and damage initiation, allowing key rate- and temperature-sensitive processes to be represented using a minimal set of physically interpretable parameters. Damage evolution is governed by an energy-based law that depends on strain rate and temperature. The model is calibrated and validated against triaxial compression and relaxation test data on polycrystalline ice, demonstrating its ability to capture salient features of ice mechanics such as ductile to brittle transitions, strain-rate-dependent strength, stress relaxation, and thermal softening. In addition, a novel healing mechanism inspired by viscous sintering is introduced, in which the rate of damage reversal is driven by viscous energy dissipation and modulated by pressure and temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".