Soil Modeling Using FEA and SPH Techniques for a Tire-Soil Interaction
Bibliographic record
Abstract
In recent years, the advancement of computerized modeling has allowed for the creation of extensive pneumatic tire models. These models have been used to determine many tire properties and tire-road interaction parameters which are either prohibitively expensive or unavailable with physical models. More recently, computerized modeling has been used to explore tire-soil interactions. The new parameters created by these interactions were defined for these models, but accurate soil constitutive equations were lacking. With the previous models, the soil was simulated using Finite Element Analysis (FEA). However, the meshless modeling method of Smooth Particle Hydrodynamics (SPH) may be a viable approach to more accurately simulating large soil deformations and complex tire-soil interactions. With both the FEA and SPH soils modeled as elastic-plastic solids, simplified soil tests are conducted. First, pressure-sinkage tests are used to explore the differences in the two soil-modeling methods. From these tests, it is found that the FEA model supports a surface pressure via the tensile forces created by the stretching of the surface elements. Conversely, for the SPH model, the surface pressure is supported via the compressive forces created by the compacting of particles. Next, shear-displacement tests are conducted with the SPH soil (as this test cannot easily be performed with an FEA soil model). These shear tests show that the SPH soil behaves more like clay in initial shearing and more like sand by exhibiting increased shearing due to vertical loading. While both the pressure-sinkage and shear-displacement tests still show that a larger particle density is unnecessary for SPH soil modeling, the shear-displacement tests indicate that an elastic-plastic material model may not be the best choice.
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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 | 0.000 |
| 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".