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Record W4384824776 · doi:10.6000/1929-5995.2023.12.06

Topological Shape Optimization Design of the Whole Bead of 265/35R18 Steel-Belted Radial Tire

2023· article· en· W4384824776 on OpenAlexvenueno aff
Yong Li, Shuang Zhang, Tao Wang, Kai Zhang, Long Chen, Shanling Han

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
Keywordsvon Mises yield criterionBeadMaterials scienceRadial tireTopology optimizationFinite element methodStructural engineeringOptimal designDurabilityComposite materialTopology (electrical circuits)Computer scienceEngineering

Abstract

fetched live from OpenAlex

The tire bead, as the most important load-bearing component at the bead area, is closely related to the durability of the tire, but its structure is developing slowly. For this reason, the topological whole bead design was proposed, although it performs well, many defects existed due to the design based on traditional experience. Therefore, this paper studies the topology shape optimization algorithm, delves into the main criterion based on von Mises and the interlaminar shear stress, and provides guidance for the structurally optimal design of the 265/35R18 radial tire whole bead. The finite element simulation results show that the von Mises of the inner end of the chafer and the end of the carcass cord are reduced by 14.48% and 24.12%, respectively. The interlaminar shear stress decreased by 28.96% and 49.51%, respectively. The von Mises of chafer and carcass cord decreased by 13.17% to 40.36% and 7.71% to 20.51%, respectively. The optimization design is of great significance to further improve the safety performance of 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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.355
Teacher spread0.286 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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