Prediction of rolling resistance and wheel force for a passenger car tire: A comparative study on the use of different material models and numerical approaches
Bibliographic record
Abstract
In this research, the characteristics of tire-road interaction of a 185/65R14 88H passenger car tire are investigated using the Finite Element Method in Abaqus commercial software. Moreover, the effect of various material models on tire performance is studied by implementing Visco-Hyperelastic, Parallel Rheological Framework, and Mullins effect. The novelty of this research is devoted to the development of the complex material models particularly considering the Mullins effect of the rubber compounds in the tire structure for the load-displacement criteria. For this purpose, a tire finite element model was generated using Abaqus/Standard command line in two different methods including an Arbitrary Lagrangian-Eulerian formulation for steady state rolling and implementing a pure Lagrangian approach for the transient dynamic analysis carried out implicit and explicit process respectively. Rolling resistance force was computed according to ISO 28580 with 210 kPa inflation pressure and 4155 N vertical load. The footprint test results were extracted in both static and transient dynamic analyses. Additionally, the wheel reaction force was predicted using an indirect method by extracting the tire-terrain contact patch reaction force in Abaqus/Explicit to observe the effect of the material convection along with stress softening phenomena of the rubber compounds of tire structure. In the post-processing analysis, the wheel reaction was filtered by implementing SAE60 filter to reduce the numerical noise in the final response.
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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 | 0.001 | 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) | 0.001 | 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 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".