Numerical Investigation of Stenosis and Degree of Aneurysm on Haemodynamics of Blood Vessels
Bibliographic record
Abstract
Computational fluid dynamics is a computer simulation technique used to study the effects of stenosis and aneurysm degree on haemodynamics in the blood vessels such as blood velocity, pressure, and wall shear stress.The current study employed the numerical simulations of pulsed blood flow using the Carreau non-Newtonian rheological and Newtonian models to model the wall shear stresses and haemodynamics in blood vessels.Furthermore, the model included three stenosis areas with different diameters, 70%, 80%, and 90%, and two aneurysm areas with different diameters, 15% and 44%.The study observed the blood velocity, pressure, and wall shear stresses at the damaged blood vessels areas.It was found that the maximum velocity was observed from stenosis when the stenosis ratio was increased to 90%.Additionally, the velocity increased up to 3.4 times, which led to increased blood shear stresses to up to 8.4 times, when the start acceleration flow and peck flow were compared.It is known that the degree of damage on blood vessels produces the most significant influence on local blood pressure and shear stresses.Therefore, it was concluded that stenosis at 90% may probably lead to serious lesions and effectively block the bloodstream by the ensuing thrombus.
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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 | 0.002 |
| 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 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".