NUMERICAL INVESTIGATION OF THE INFLUENCE OF THE LINK POSITIONING IN THE CORONARY STENT INSIDE THE NORMAL ARTERY: A COMPARATIVE STUDY OF TWO COMMERCIAL STENT DESIGNS
Bibliographic record
Abstract
This paper investigates the performance of two commercial stent designs inside the normal artery for induced Von Mises Stress and radial displacement pattern. Investigation focuses on identifying the key design feature of the stent structure responsible for varied stress and displacement pattern. Two commercial stent designs, Supraflex (Stent S) and Yukon Choice (Stent T),are modeled using micro CT images and MIMICS® while idealized models are used for investigation. ANSYS Workbench is used to numerically expand the stent inside an idealized normal artery with inflation pressure. The stent and the artery are modeled using elastic-plastic and hyperelastic material models, respectively. The results suggest crucial influence of the link positioning in inducing an area of higher Von Mises Stress and stress gradient. The locations of a higher stress gradient are those in line with unbound stent crowns. Also, higher and uniform arterial displacement can be observed in the locations in line with the bound crown. Results also suggested a considerable difference in arterial distortion induced by two designs, causes for which can also be attributed to the differences in the link placement. The study suggests that the link connections play a crucial role in setting up stress field/radial displacement. Suitable modification of the link positioning can reduce the higher stress gradient and arterial distortion, which probably can reduce arterial injury.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".