Sensitivity analysis of truck tyre hydroplaning speed using FEA-SPH model
Bibliographic record
Abstract
The hydroplaning phenomenon is a complex multi-physics problem that may affect any vehicle under wet road conditions. It is crucial to understand hydroplaning phenomenon to improve passenger safety on highways. This paper focuses on studying the hydroplaning of different truck tyres. The study includes the effect of inflation pressure, load, tread depth and water film thickness on hydroplaning speed. The tyres used in this study are the Goodyear's off-road RHD 315/80R22.5 tyre and the UOIT FEA Michelin XOne line energy T 445/50R22.5 tyre. The analysis is performed using an FEA code named Pam-Crash from ESI Group. Water is modelled using Murnaghan equation of state and smooth particle hydrodynamics (SPH) method. The simulation results are validated against Horne's equation for truck tyres. An empirical equation is developed to include various tyre parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".