Bibliographic record
Abstract
Ice friction affects us in many ways, from slippery roads to winter sports. In cold regions, ice friction influences ice interaction with itself, which determines the motion of ice floes. It also influences the structural forces resulting from ice interactions with fixed and moored structures and with floating vessels. Ice friction also affects surface transportation over snow and ice. This paper addresses only one aspect of ice friction in winter sports, but it is potentially relevant to other applications, particularly surface transportation over ice. The model of ice friction described here is for a steel bobsleigh runner sliding on ice at high velocity. The model describes ice friction in the fully-lubricated, hydrodynamic regime, where a layer of meltwater completely separates the ice and slider surfaces. The effect of any contact between asperities on both surfaces is neglected. Friction results from a ploughing force, arising from ice deformation, crushing and extrusion, and from the shear stress in the lubricating Couette flow. The model takes into account frictional melting, heat conduction into the ice and the lateral squeeze flow of the lubricating liquid. The effect of pressure on the melting temperature is also accounted for. Sensitivity testing of the numerical model has been conducted to examine the influence of such factors as runner dimensions, sliding speed, ice temperature and g-forces. A comparison with recent measurements of bobsled ice friction made by one of the authors is encouraging, suggesting that the model has identified and adequately represented the most essential physical processes. Copyright © 2013 by the International Society of Offshore and Polar Engineers (ISOPE).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".