Comparative Analysis of Non-Pneumatic Tire Spoke Designs for Off-Road Applications: A Smoothed Particle Hydrodynamics Perspective
Bibliographic record
Abstract
This study explores the development of a terramechanics-based model for non-pneumatic tire–terrain interaction, focusing on different spoke designs. The research investigates how four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) affect non-pneumatic tire performance in off-road conditions. Using the finite element method (FEM) to model non-pneumatic tires, and smoothed-particle hydrodynamics (SPH) to model dry, loose soil, simulations were conducted to replicate real-world loading conditions. This study utilizes virtual environment solution finite element analysis software to examine the interaction between a non-pneumatic tire and dry, loose soil, with a focus on calculating longitudinal and vertical forces. These forces play a pivotal role in determining the motion resistance coefficient. The results show distinct variations in the motion-resistance coefficients among the spoke designs on dry, loose soil. This analysis helps to identify the spoke configurations that optimize energy efficiency and fuel consumption. By comparing and evaluating the four spoke designs, this study shows the effect of spoke design on tire motion resistance. This study concluded that the modified honeycomb spoke design is the most stable and the least sensitive to operating conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".