Study of blood flow in stenosed artery model using computational fluid dynamics and response surface methodology
Bibliographic record
Abstract
Abstract Stenosis is a type of cardiovascular disease (CVD) which involves the deposition of plaque on the inner wall of the artery, leading to narrowing of the artery, abnormal blood flow patterns and higher wall shear stresses (WSS), which contributes to high blood pressure, thrombus formation, and other chronic diseases. Chemical engineers can play a vital role in the area of haemodynamics using experimental and computational fluid dynamics (CFD) tools. WSS is affected by different values of severity of stenosis and length of stenosis. Hence there is a need for obtaining a quantitative relationship that can easily allow anyone to study flow dynamics in stenosis by studying WSS. In this study, an attempt has been made to develop such a relationship using CFD simulations and response surface methodology (RSM). The design parameters considered in this study are stenosis severity of 10%, 25%, 50%, 75%, 90%, and D/L s (diameter of artery/length of stenosis) of 0.2, 0.3, 0.4, 0.5, and 0.6. WSS and pressure drop across the stenosis were used as a response to assess the effect of these parameters. Blood flow inside the stenosed artery was modelled using large eddy simulation (LES) to predict the turbulent flow fields in the stenosed artery model. Based on the simulation results, correlation of WSS as a function of severity, length of stenosis, and peak Reynolds number was obtained using RSM.
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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 | 0.001 |
| 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) | 0.000 | 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".