Pattern Design of Non-Pneumatic Tire for Stiffness Using Topology Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Non-pneumatic tires have been developed and being investigated, but not much prevalent. Many design studies are yet needed from the viewpoint of material, pattern, and structures. No systematic research for such important design issues have been reported in the literature. In this paper, as the first important step of design, topology optimization was utilized to determine optimal topological patterns of non-pneumatic tires, with the goal of matching the static stiffness of the current pneumatic tires. Under the optimization formulation with weighted compliance and a volume constraint, several different patterns were obtained depending on the number of patterns, volume fraction, and weighting factors. Among them, three representative patterns were chosen and analyzed for their possible applications under specific working condition. This paper proposes a systematic and efficient tool for designing the topological patterns of non-pneumatic tires.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it