Simulation and Test Correlation of Wheel Impact Test
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The wheel impact test evaluates wheel structural performance for a typical lateral curb impact event occurring in passenger cars and light trucks. This test which is as per SAE J -175 standard has a striker dropped from a specified height on to a fixture mounted wheel-tire assembly. This impact test performance is critical to meeting overall structural performance for the wheel. There are many processes and methods available to simulate impact tests using FE analysis and in this study, certain existing methods are fine tuned to facilitate improved correlation with aforementioned lab test. Abaqus explicit is used in the simulation process and FE analysis-test correlation is achieved within 3% (strain gauge measurements). The improved method closely captures the behavior of the wheel during and after impact including capturing the variation of bolt pretension during the impact test. The wheel width before and after impact is another parameter used to compare analysis and test results. Further, the contribution of impact load between the wheel and tire is studied, to support the modeling strategy used in this new method.</div></div>
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".