Finite Element Modeling of Tire With Validation Using Tensile and Frequency Response Testing
Bibliographic record
Abstract
A physical testing program is performed in support of finite element model creation for a 50-series passenger car tire. ABAQUS finite element analysis software is used along with its standard material models. Uniaxial tension testing of tire samples cut from the tread composite, tread rubber and sidewall composite is performed in order to obtain material properties. Hyper-elastic material coefficients for tread rubber are fit using uniaxial tension test data. Results show that the Arruda-Boyce hyper-elastic material model fits the test data well and it predicts reasonable overall behavior in uniaxial tension and uniaxial compression. Most other hyperelastic material models are found to predict unrealistic behavior in uniaxial compression for the tire samples, especially in the 0 to 20% compressive strain range. Frequency response testing of two inflated passenger car tires of different sizes, makes and models is also performed to assist in defining the viscoelastic material model for tread rubber. Test results show that tire modal damping is in the 2 to 4% range for most modes below 200 Hz, and the response curves, modal density and modal damping are remarkably similar for the two tires tested. The tire finite element model with updated material properties is simulated for nine combinations of air inflation pressure and vertical load in order to calculate static loaded radius. The analysis results are compared with physical test results and the analysis results are found to deviate at most by 3% compared to the tests.
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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 | 0.000 | 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".