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Record W2010435438 · doi:10.5539/mas.v6n4p23

Numerical Analysis of the Influence of Stent Parameters on the Fatigue Properties

2012· article· en· W2010435438 on OpenAlexvenueno aff
Lin Chen, Shen Jingfeng, Bing Chen

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsnot available
FundersShanghai Leading Academic Discipline Project
KeywordsFinite element methodPulsatile flowMaterials scienceStentFatigue testingStructural engineeringFatigue limitLimit (mathematics)Low-cycle fatigueBlood flowComposite materialSurgeryMathematicsRadiologyEngineeringMedicineCardiology

Abstract

fetched live from OpenAlex

Vascular stents are small cylindrical structures implanted in stenosed vessel to support vessel wall and restore blood flow. Cycle fatigue properties of Nitinol stents is an essential feature. Structural parameters make great impact on cycle fatigue properties of Nitinol- based vascular stents. The primary aim of the paper was to investigate the effects of varying parameters of “struct V” on the pulsatile fatigue by using the finite element method (FEM). The finite element method model to analyse the fatigue properties of stent was dependant on different length, width or thickness. The results show that increasing the length, the width and the thickness could reduce the possibility of fatigue, but beyond a certain limit may play an opposite role. In conclusion, FEM is necessary tools in designing stents.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.259
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2012
Admission routes1
Has abstractyes

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