Development of In-Plane Truck Tire-Flooded Surface Interaction Models Using FEA-SPH Techniques
Bibliographic record
Abstract
The performance of a vehicle highly depends on the tire-terrain interaction characteristics. The terrain on which a vehicle operates can vary dramatically. This paper focuses on the evaluation of an in-plane truck tire performance running over the flooded surface. The truck tire is modeled using Finite Element Analysis (FEA) technique and validated against measured data. The water is modeled using Smoothed Particle Hydrodynamics (SPH), which includes water material properties. The tire-terrain interaction algorithm is defined using node-symmetric node-to-segment contact with edge treatment. The performance characteristics of the interaction include the rolling resistance coefficient, vertical, longitudinal tread and longitudinal tire stiffnesses. The simulations are repeated for several operating conditions such as inflation pressure, applied vertical load, and water depth. The flooded surface results are compared with previously published data. This work will be extended to include the prediction of the full in-plane and out-of-plane rigid ring tire model parameters while the tire is operating under various conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".