A finite element model on effects of impact load and cavitation on fatigue crack propagation in mechanical bileaflet aortic heart valve
Bibliographic record
Abstract
Pyrolytic carbon mechanical heart valves (MHVs) are widely used to replace dysfunctional and failed heart valves. As the human heart beats around 40 million times per year, fatigue is the prime mechanism of mechanical failure. In this study, a finite element approach is implemented to develop a model for fatigue analysis of MHVs due to the impact force between the leaflet and the stent and cavitation in the aortic position. A two-step method to predict crack propagation in the leaflets of MHVs has been developed. Stress intensity factors (SIFs) are computed at a small initiated crack located on the leaflet edge (the worst case) using the boundary element method (BEM). Static analysis of the crack is performed to analyse the stress distribution around the front crack zone when the crack is opened; this is followed by a dynamic crack analysis to consider crack propagation using the finite element approach. Two factors are taken into account in the calculation of the SIFs: first, the effect of microjet formation due to cavitation in the vicinity of leaflets, resulting in water hammer pressure; second, the effect of the impact force between the leaflet and the stent of the MHVs, both in the closing phase. The critical initial crack length, the SIFs, the water hammer pressure, and the maximum jet velocity due to cavitation have been calculated. With an initial crack length of 35 microm, the fatigue life of the heart valve is greater than 60 years (i.e. about 2.2 x 10(9) cycles) and, with an initial crack length of 170 microm, the fatigue life of the heart valve would be around 2.5 years (i.e. about 9.1 x 10(7) cycles). For an initial crack length greater than 170 microm, there is catastrophic failure and fatigue cracking no longer occurs. A finite element model of fatigue analysis using Patran command language (PCL custom code) in MSC software can be used to evaluate the useful lifespan of MHVs. Similar methodologies can be extended to other medical devices under cyclic loads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".