Effect of non-linear leaflet material properties on aortic valve dynamics - A coupled fluid-structure approach
Bibliographic record
Abstract
Due to complex structure of aortic valve (AV) leaflets and its strong interaction with the blood flow field, realistic and accurate modeling of the valve deformations comes with many challenges. In this study, we aimed to investigate the effect of AV material properties on the valve deformations, by implementing different non-linear properties of the AV leaflets in three different material models. In the computations, we captured the dynamics between the leaflet deformations and blood flow field variations by using an iterative implicit fluid-structure interaction (FSI) approach. By comparison of the FSI simulation results of these three models, the effects of hyperelasticity and anisotropy on the valve deformations have been studied in detail. The simulation results reveal the fact that the material characteristics strongly affect the deformation characteristics of the leaflets in the systolic phase. The material anisotropy stabilizes the leaflet movements during the systolic phase, which helps decreasing the flutters of the leaflets during the peak jet blood flow. Similarly, it has been observed that the hyperelastic behavior yields an increase in the valve opening area during systolic phase which prevents the risk of excessive work of the heart due to high pressure difference. Furthermore, simulation results indicate that the stress levels in hyperelastic model are much lower, compared to the stress levels in linear elastic one. This suggests that the non-linear material character of the leaflets decreases the risk of calcification.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".