Modelling of Off-Road Truck Tire-Rim Slip Using Finite Element Analysis
Bibliographic record
Abstract
Slip or relative rotation between the tire and rim is a significant concern for vehicle operation and wheel manufacturing since it leads to wheel imbalance and vibration as well as power losses. A slip situation typically occurs due to improper bead lubrication and mounting, irregularities in the bead seat, and extreme loading conditions with high torques and low tire pressures. Currently, there are relatively few published studies on the tire-rim interface, and they mainly focus on topics such as the mounting process, load transfer, and friction modelling. This leaves a gap to explore the measurement and variation of gross tire-rim slip under the dynamic conditions of a driven tire. In this paper, a previously developed and validated FEA truck tire model was modified to include a frictional contact surface between the tire and rim, and then the slip ratio between the tire and rim was measured under different operating conditions. The new model’s static behavior was compared to the previously validated model with the fixed tire-rim connection, then the model parameters such as vertical load, inflation pressure, drawbar load, longitudinal wheel speed, and tire-rim friction were varied and their effects on the tire-rim slip of the driven truck tire were investigated. It was discovered that factors affecting the torque transmitted through the wheel, including the tire-rim friction, vertical load, and inflation pressure, had the most significant effect, while the longitudinal wheel speed had a minimal effect on the tire-rim slip. The findings of this research work will be useful for determining the tire-rim slip under various operating conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".