A Model of Ice Friction for Skeleton Sled Runners
Bibliographic record
Abstract
A numerical model of ice friction for the runners of a skeleton sled has been devised. The skeleton runner consists of a standard stainless steel rod of approximately 16 mm diameter. Two grooves are machined into the trailing half of the runner. The distance between the grooves is approximately 1mm, giving rise to a spine or blade at the centre of the runner. The skeleton sled has no apparent mechanism for steering. Hence, steering is accomplished by the athlete, using either lateral air drag forces (tilting the helmet), dragging a toe, or by attempting to make the spine of the runner “dig into” the ice more on one side than the other. Until now, the physics of the latter steering mechanism has been poorly understood. It has been assumed that when the spine “digs into” the ice, the ice friction increases. Our numerical model calculates the details of the contact footprint of the runner on the ice. It also considers frictional heating, heat conduction into the ice and lateral squeeze flow in order to calculate the ice friction coefficient, assuming fully lubricated friction conditions. The model suggests that skeleton sliding can occur in two regimes. The first is one where the sides of the spine do not contact the ice. The second occurs when the spine “digs into” the ice and the sides of the spine contact the ice. By exploring the second regime, we have shown that, as the contact area between the sides of the spine and the ice increases, the ploughing force increases, in accordance with the traditional explanation of steering. However, the shear stress force in the lubricating layer also increases, resulting in a significantly higher ice friction coefficient for the runner with the longer spine contact. This result provides scientific evidence to support the athlete’s experience, that by engaging more of the spine by torqueing the frame of the sled, it is possible to steer the sled, using the differential ice friction on the left and right runners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".