Influence of sand moisture content on mixed service truck tire performance using advanced hybrid techniques
Bibliographic record
Abstract
This paper focuses on investigating the effect of sand moisture content on the performance of a Mixed Service Drive (MSD) truck tire of size 315/80R22.5. The truck tire is modeled using multiple layers and materials within a Finite Element Analysis (FEA) environment. Moist sand is modeled using the Smoothed-Particle Hydrodynamics (SPH) technique. Initially, dry sand is modeled using a hydrodynamic elastic-plastic material, while water is modeled using the Murnaghan equation of state. The numerical interaction between the sand and water is identified using Darcy’s law. The moisture content of the sand is then calibrated using direct shear-strength tests and validated against physical measurements conducted in a laboratory under similar sand conditions. The tire-sand interaction is defined using a hybrid FEA-SPH interaction model, and a non-symmetric node-to-segment contact with edge treatment contact algorithm. The tire tractive performance including tractive effort, motion resistance coefficient, and tire sinkage was examined under various operating conditions, including different sand moisture content levels and tire longitudinal speeds.
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 | 0.001 |
| 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".