Development of FEA tyre/soil interaction model using SPH and hybrid SPH/FEA technique
Bibliographic record
Abstract
One of the most accurate methods for simulating numerous nonlinear systems is finite element analysis (FEA). However, when it comes to modelling non-meshed particles, smoothed particle hydrodynamics (SPH) is used to obtain more reliable results. The modelled FEA truck tyre is similar to a wide base truck tyre having specifications of 445/50R22.5. The tyre is previously validated through different simulations such as contact foot print, static deflection, dynamic first mode of vibration, and various rolling resistance tests. These simulations are performed under different inflation pressures, vertical loads, and speeds. In this research, the tyre is validated on soft soil, while considering various combinations of parameters. These parameters affect soil characteristics to reach an optimum and calibrated soil model corresponding to the existing empirical data for dry sand. This research will be heading toward developing an off-road rigid ring model in future projects.
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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 | 0.000 |
| 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 | 0.000 |
| 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".