The correlation research between ground contact characteristics and rolling resistance of a tire
Bibliographic record
Abstract
This study was aimed to find out the correlation between the ground contact characteristics and rolling resistance of a tire. To accomplish the goal, we used 40 sets of tires with the different rolling resistance to examined the tire ground contact characteristics and rolling resistance coefficient of the vehicles in motion. This research team assessed the tire ground contact characteristics with a dynamic contact pressure measurement system equipped with small 3-axis force sensors and 6-axis force & moment sensors, analyzing the contact patch shapes, 3-axial forces (Fx, Fy, Fz), force distribution, and vectors, which practically occur on a real track. In addition, the rolling resistance of a tire was tested according to Force Method as stipulated in ISO-28580, using the equipment (KOBE Co.). The test result showed that the magnitude of Force X among tire ground contact characteristics, which is applied in the direction a car moves, is highly correlated with rolling resistance and the rest (Force Y, Force Z, Contact Patch Shape, and vectors) turned out not significantly correlated with rolling resistance. In addition, when the characteristics of ground contact and resistance by air pressure were compared, a high correlation with Force X was reconfirmed. Moreover, coasting test confirmed the actual effect of tire rolling resistance upon the running resistance of a vehicle. In this study, we analyzed the correlation between tire ground contact characteristics and rolling resistance and experimentally derived major performance factors. In the future, we plan to conduct other experiments regarding the correlation between tire ground contact characteristics and rolling resistance in various driving environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".