Development and validation of a Regional Haul Steer (RHS) truck tiremodel using advanced computational techniques
Bibliographic record
Abstract
Proper tire modeling is essential for a good vehicle design, as tires play an important role in vehicle dynamics and road safety, and are the vehicle’s main point of contact with the road. There are studies based on a variety of tire models for different applications, but there is a lack of research related to Regional Haul Steer (RHS) truck tires. A finite element model of a 315/80R22.5 Goodyear RHS II tire was created, and the model was validated using static (vertical stiffness and footprint) and dynamic (drum cleat and cornering stiffness) tests. It was observed that the increase of inflation pressure resulted in a decreased tire contact area, while various other parameters showed an increase, such as the vertical stiffness and the first horizontal and vertical vibration modes. The values obtained from the aforementioned simulation test results were compared with the literature and experimental data to validate the RHS tire model for use in future research. The values obtained from the tests were in good agreement with the experimental data provided. Future work includes the determination of cornering characteristics and rolling resistance over hard surfaces and various terrains, and the training of a genetic algorithm model for use in full vehicle simulations.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".