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Record W2007926198 · doi:10.1115/detc2009-86068

Pattern Design of Non-Pneumatic Tire for Stiffness Using Topology Optimization

2009· article· en· W2007926198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 6: ASME Power Transmission and Gearing Conference; 3rd International Conference on Micro- and Nanosystems; 11th International Conference on Advanced Vehicle and Tire Technologies · 2009
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsTopology optimizationWeightingStiffnessConstraint (computer-aided design)Computer scienceMatching (statistics)Topology (electrical circuits)Volume (thermodynamics)Mathematical optimizationEngineeringMechanical engineeringMathematicsStructural engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.276
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it