Sensitivity Analysis of Tire-Soil Interaction Using Finite Element Analysis and Smoothed Particle Hydrodynamics Techniques
Bibliographic record
Abstract
This paper presents the modelling, calibration and sensitivity analysis of LETE sand soil using Visual Environment’s Pam Crash. LETE sand is modelled and converted from Finite Element Analysis mesh (FEA) to Smooth-particle hydrodynamics (SPH). The sand is then calibrated using terramechanics published data by simulating a pressure sinkage test and shear box test using the SPH LETE sand particles. The material properties such as tangent modulus, yield strength and bulk modulus are configured so the simulation’s results match those of theoretical values. Sensitivity analysis of the calibrated LETE sand material is then investigated. The sensitivity analysis includes mesh size, plate geometry, smoothing length, max smoothing length, artificial viscosity and contact thickness. The effect of these parameters on the sand behavior is analyzed. Finally, SPH LETE Sand is used to determine the rolling resistance coefficient of FEA off-road truck tire size 315/80R22.5 for different mesh size sand particles. The results found within this paper will be continued in regard to achieving better understanding of vehicle dynamics for tire-terrain interaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".