Sensitivity analysis of truck tire tread material properties for on-road applications
Bibliographic record
Abstract
This research delves into the sensitivity analysis of a truck tire rubber compound concerning its impact on tire–road interaction characteristics. Initially, the study employs finite element analysis to model a 315/80R22.5 truck tire, which is subsequently validated through static and dynamic response assessments via various simulation tests. Following validation, the established tire model is utilized to conduct a sensitivity analysis of the tire rubber compound specifically applied on the tread. This analysis encompasses several material definitions, including Mooney–Rivlin, visco-Mooney–Rivlin, linear viscoelastic, and nonlinear viscoelastic materials. By exploring the effects of these material models, the research scrutinizes their influence on tire–road interaction characteristics across diverse operating conditions. The tire–road interaction characteristics include the rolling resistance coefficient, and the cornering force at operating conditions including the longitudinal speed, vertical load, and slip angle. This comprehensive investigation offers insights into the intricate relationship between tire composition and performance, thereby enhancing our understanding of tire behavior and informing potential advancements in tire technology.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".