Comparative Analysis of Non-Pneumatic Tire Spoke Designs for On-Road Applications: A Traction Force Perspective
Bibliographic record
Abstract
Abstract This study explores the traction performance and structural integrity of Non-pneumatic (NP) tires, which are being explored as alternatives to traditional air-filled tires, offering enhanced durability, reduced maintenance, and improved eco-friendliness. This study investigates the influence of four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) on NP tire traction performance for on-road applications. Using Finite Element Analysis (FEA), the study considers the influence of longitudinal speed and vertical load on the traction performance and stress distribution of the NP tire designs. This assessment of traction coefficient under the constant torque will reveal distinct variations in performance across the four-spoke designs. By systematically varying these parameters, the study aims to provide a detailed understanding of how different design factors influence the overall performance of NP tires. Furthermore, the traction coefficient measurements helped to identify spoke configurations that optimize tire-road interaction performance. Comprehensive comparison and evaluation of the four NP tire spoke designs highlight their respective advantages and limitations for on-road applications. By optimizing spoke shapes and considering the effects of speed and load, manufacturers can develop NP tire designs that offer improved traction, durability, and efficiency for a wide range of automotive applications. These findings provide valuable insights for optimizing NP tire designs, aiding decision-making for manufacturers in the automotive industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".