Modeling of Tire-Wet Surface Interaction Using Finite Element Analysis and Smoothed-Particle Hydrodynamics Techniques
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper focuses on predicting the rolling resistance and hydroplaning of a wide base truck tire (Size: 445/50R22.5) on dry and wet surfaces. The rolling resistance and hydroplaning are predicted at various inflation pressures, loads, velocities, and water depths. The wide base truck tire was previously modeled and validated using Finite Element Analysis (FEA) technique in virtual performance software (Pam-Crash). The water is modeled using Smoothed-Particle Hydrodynamics (SPH) method and Murnaghan equation of state. A water layer is first built on top of an FEA rigid surface to represent a wet surface. The truck tire is then inflated to the desired pressure. A vertical load is then applied to the center of the tire. For rolling resistance tests variable constant longitudinal speeds are applied to the center of the tire. The forces in the vertical and longitudinal directions are computed, and the rolling resistance is calculated. The effect of the longitudinal speed on rolling resistance on the wet surface also includes the prediction of the hydroplaning speed. The results are analyzed, and the effect of each parameter is investigated.</div></div>
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".