Large-displacement 3D structural analysis of an aortic valve model with nonlinear material properties
Bibliographic record
Abstract
Grafts used in aortic valve-sparing procedures should ideally not only reproduce the geometry of the natural aortic root but also its material properties. Indeed, a number of studies using the finite element method have shown the importance of the natural sinus shape of the root in the functioning of the normal aortic valve, and the relative increase in stresses due to the replacement of the valve by a stiffer synthetic graft. Because of the wide range in experimentally measured values of aortic wall and leaflet material properties, studies by different research groups have incorporated very different material properties in their models. The aim of the present study was to investigate the influence of material properties on aortic wall displacements, and to determine which material properties would most closely match reported experimental data. Two geometrically accurate 3D models corresponding to the closed and open valve configurations were created in Pro/Engineer CAD software. Loads corresponding to systolic and diastolic pressures were specified and large-displacement structural analyses were carried out using the ANSYS package. Results have indicated that the closest match to experiments using isotropic material properties occurred for a Young's modulus of about 2000 KPa. Nonlinear models based on experimental stress-strain curves have shown similar displacements, but altered strain distribution patterns and significantly lower stresses. These results suggest that an accurate comparison of potential new graft models would have to be made with natural aortic valve models incorporating nonlinear material behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".