Predicting Hemodynamic Alterations Due to Bifurcation Stenosis: A Mathematical Model and Numerical Analysis of Newtonian Blood Flow
Bibliographic record
Abstract
Modeling blood flow through a stenosed bifurcated artery is crucial in the biomedical field, as it provides valuable insights into how these conditions affect the body and contribute to the diagnosis, treatment, and prevention of cardiovascular diseases.The Marker and Cell (MAC) method is used to numerically solve the equations, and the code was created using MATLAB programming to obtain and analyze the flow characteristics-including velocity profiles, flow patterns, and pressure distributionthat have not been thoroughly investigated in earlier studies in this field.The benefit of the MAC approach is that it eliminates the need for pressure boundary conditions at either the inlet or outflow.A graphic representation of these characteristics, along with relevant physical parameters, is provided in this study.The results indicate that the severity of the stenoses leads to an increase in the recirculation zones in the junction and the outer wall in the daughter artery, as well as the wall pressure rapidly drops at the throat of the stenosis.Also, as the blood passes through the stenosis, its flow is accelerated.Then, after the narrowest point of the stenosis, the artery returns to its normal, wider diameter.High-velocity flow stream, however, continues to move forward in a straight line, leaving a low-pressure region between the streaming blood and the artery wall.The blood in the low-pressure zone is pulled backward, and it starts to swirl in a circular motion to fill this space.This swirling motion generates the recirculation zones, which are widely recognized as a significant factor in the progression of cardiovascular disease.The current findings show good agreement with established results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".