Development of Truck Tire-Soil Interaction Model using FEA and SPH
Bibliographic record
Abstract
Modern Finite Element Analysis (FEA) techniques allow for accurate simulation of various non-linear systems. However they are limited in their simulation of particulate matter. This research uses smooth particle hydrodynamics (SPH) in addition to FEA techniques to model the properties of soils, which allows for particle-level replication of soils. Selected soils are simulated in a virtual environment and validated using the pressure-sinkage and shear tests. A truck tire model is created based on standard heavy vehicle tires and validated using static deflection, contact footprint, and dynamic first mode of vibration tests. The validated tires and soils are used to create a virtual terrain and the tire is placed on the soil, loaded, and run over the soil at various speeds. The results of these simulations show that the SPH modeling technique offers higher accuracy than comparable FEA models for soft soils at a higher computational cost. Further, the SPH models show rolling resistance and tire forces in good correlation with available experimental data. The purpose of performing this research is to develop an off-road rigid ring model in future work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".