Ice Adhesion Models to Predict Shear Stress at Shedding
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Bibliographic record
Abstract
Abstract The model presented in this paper is the first step towards explaining the mechanisms of ice adhesion. Considerable work, however, remains to validate each term included in the model due to the lack of physical constants and parameters related to rough surfaces. The ice adhesion model at the ice-substrate interface is based on water behavior before and after freezing, substrate roughness and ice type. Within nanoseconds following impact, water occupies the substrate surface either partially before freezing when drops or rivulets form, or totally when a film is formed. The ice surface area in contact with the substrate is reduced due to the space between drops and rivulets. Due to the electrostatic attraction between water and substrate molecules, ice sticks to the substrate. The electrostatic force depends on the intensity of this bond, which is related to the work needed to maintain the drop shape over the surface and the distance between the water and substrate molecules. The water can also sink in and fill the cavities formed by adjacent surface roughness peaks when the surface tension force is less than the water pressure force. Following the phase change, on the order of microseconds for rime ice, and milliseconds for glaze ice, the ice mechanically locks onto the surface and must be broken down to be shed. This paper shows the development of a phenomenological model to predict the cohesive failure of ice, one that does not take into consideration rime ice porosity. The model assumes that ice near its freezing point is subject to internal and external strains, and that its cohesive strength corresponds to the failure stress. The failure stress is dependent on grain size and creep involving grain boundary sliding in a polycrystalline material at elevated temperatures. The next steps in the development of the model are to quantify the physical parameters, validate an idealized rough surface, as well as evaluate the effects of rime ice porosity and small grain sizes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it