Fluid-StructureInteractionSimulationOfTheMitralValveInANormal LeftVentricleDuringDiastolicPhase
Bibliographic record
Abstract
In this paper, the blood flow inside a normal (healthy) left ventricle was investigated, using loosely coupled fluid-structure interaction (FSI) algorithm during diastole. The model consists of the mitral valve (MV) and cavity of the left ventricle (LV) as illustrated in figure 1. Parametric geometries of the left ventricle and mitral valve were developed using MR images and pathological data. To apply pressure boundary condition to the aorta, we simulated the systematic arterial tree by characterizing the Windkessel model [1]. The Alexander model [2] was used to apply the left atrial flow to the mitral valve. The simulation started at the beginning of the systole by moving the left ventricular wall inward to push the blood flow out of the LV toward aorta. The displacement of the wall was transfered to the fluid mesh and the Navier-Stoks equations were solved using finite volume with SIMPLE-C method. We applied the calculated pressure to the surfaces of MV leaflets and the structural model of the MV tissue [3] was solved using finite element method. At the end of the cycle, we transfered the calculated deformationsoftheleafletstothefluidmesh.Forthenexttimestep,theleftventricularwallwasmoved further and the same procedure was repeated until the end of systole. In the diastolic phase the aorta was closed and the transmitral flow was applied to the MV orifice. Also the left ventricular wall was moved backward at each time step and the same FSI cycle was used to calculate the blood flow and MV deformation in diastole. We characterized the creation of the vortex ring by the pulse jet which was produced in the early diastole. At the beginning of the diastole a vortex ring is separated from the boundary layer at the tip of the leaflets. Our study shows that no more energy contributes in the vortex ringifweincreasetheformationtime(whichdescribestheformationofthevortexring)higherthan4.3 andafterthispoint,theflowcontributesinthetrailingjet.Wealsostudiedtheeffectofthemotionofthe leaflets on the formation of the vortex ring and finally, we compared our results with the experimental works in which similar phenomenon was studied [4 and 5].
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".