Finite Element Modelling of Blood Flow in Artery Stenosis
Bibliographic record
Abstract
Recent work by our laboratories suggest that the endothelial cells that line blood vessels respond dramatically to shear stress gradients over millimeter and micrometer length scales, contributing to the progression of the atherosclerosis disease state. In this paper, we present a CFD model for the prediction and quantitative analyses of the hemodynamic behavior of blood flow in stenosed arteries of a guinea pig, comprised of a nearly axisymmetric vessel constriction. The blood is considered to be incompressible and the flow model is described by the Navier-Stokes and the continuity equations. A standard Galerkin finite element technique has been applied for the solution of the flow equations within a 2-D axisymmetric framework. Elemental discretization is based on the use of C0- continuous Taylor-Hood type isoparametric finite elements that are used for the approximation of the unknown field variables. An implicit-theta time-stepping scheme has been chosen for the temporal discretization of the flow equations. The rheological behaviour of blood is incorporated within the main flow model through the use of different non-Newtonian constitutive equations. Relationships of the stenosis severity and flow data such as flow rate and flow pressure are obtained from the numerical simulations. The results are presented in the form of velocity vectors and pressure surface plots and are examined for stability, convergence and theoretical consistency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".