Impact Testing of Passenger Vehicle and Semi-Truck Pneumatic Tires and Rims
Bibliographic record
Abstract
Wheels and tires on vehicles, are often directly (or indirectly) involved in collisions with other vehicles or fixed objects. In this study, the effects of the pneumatic tire and rim, as it contributes to a dynamic collision, was isolated and studied. A total of 15 mounted tires of various common sizes were selected to conduct 35 dynamic impact tests into the flat face of an instrumented concrete barrier. The tires and rims used in the tests ranged from heavy truck, light truck, down to common passenger vehicle tires. Each of the 15 tires and rims were impact tested individually to failure in order to explore the dynamic response and performance of pneumatic tires in collisions. Of the 35 tests, 28 were conducted with a single tire and rim configuration and 7 tests were conducted simulating a dual truck tire configuration. It was determined that the coefficient of restitution for 22 of the tire impacts into the rigid flat faced barrier were remarkably similar, around 0.9 ± 0.1. Additionally, three full-scale impact tests were conducted using four heavy truck tires, mounted in parallel with offsets typical of dual tire spacing, to impact the front, rear, and side of a mid-size SUV. The coefficient of restitution ranged between 0.31-0.33 for all three impacts. The acceleration and integrated velocity pulses of those same three tests were also compared with previous published research of narrow frontal object impacts and found to have good correlation. These instrumented pneumatic tire and rim tests provide well-documented real-world type tests that can be used for future evaluation, comparison, or simulation of similar applicable type collision events. They also provide a foundational basis for future exploration of the role of the pneumatic tires directly involved in impacts.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".