Modeling and Validation of Compressed Snow–Tire Interactions for Traction Assessment Under Controlled Slip Conditions
Bibliographic record
Abstract
Abstract Snow traction is an important performance parameter for tire manufacturers, and it is evaluated using a standardized test (ASTM F1805-20). In this test, the tire driving traction on compressed snow is recorded under controlled conditions. However, conducting multiple tests is challenging due to the limited availability of proving grounds and the difficulty of maintaining consistent test conditions. Additionally, prototyping and testing are costly. To address these limitations, this paper investigates the modeling and prediction of snow–tire interaction to estimate the traction coefficient of standard reference test tire (SRTT) 225/60R16 at different slip ratios, and it is validated against in situ test data. The SRTT tire is modeled using finite element analysis (FEA) and validated under static loading conditions. The validation considers radial deflection, footprint area, and contact pressure at different inflation pressures against experimental data. Compacted snow is modeled using Drucker–Prager cap (DPC) plasticity material model and a hybrid smoothed particle hydrodynamics-finite element method (SPH-FEM) approach. The DPC model captures the material behavior of compacted snow accurately, while the hybrid SPH-FEM approach is computationally efficient. The study evaluates the traction performance of the SRTT tire on compacted snow for different slip ratios and compares the results with in situ test data. Furthermore, the impact of sipes on traction performance is analyzed by comparing a SRTT tire model with a blank-rib tire model under identical slip conditions. The findings contribute to enhancing traction modeling methodologies for virtual validation of winter tires.
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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".