Constitutive modeling and validation of a racing slick tire model in a finite element environment
Bibliographic record
Abstract
This paper presents the modeling and validation of a Hoosier R25B 18X6.0-10 racing slick tire using a Finite Element Analysis (FEA) environment. In parallel, the work shows a method of efficiently developing an FEA tire model for tire-road interaction estimation. To overcome limitations and the absence of material data from rubber and tire manufacturers, constitutive modeling of various tire parts is performed. Experimental validation for the tire’s constitutive modeling was performed through uniaxial tension tests with the ASTMD412 standard specimens and stress relaxation tests using the DMA TAQ800. This was repeated for various parts of the tire where the specimens were cut in perpendicular directions. The modeling of the tire uses solid elements in layers that interchange the tire materials in perpendicular directions. A comprehensive validation process was executed through static deflection at different camber angles, drum cleat, and rolling resistance tests. The FEA Hoosier R25B tire model simulation results demonstrated excellent agreement with the experimental tests within errors below 6%. This study outlines an efficient method to provide a robust FEA tire model that captures race tires’ complex mechanical tire-road interactions and contributes to advanced tire design and simulation tools for automotive applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".