Study of the effect of stenosis severity and non-Newtonian viscosity on multidirectional wall shear stress and flow disturbances in the carotid artery using particle image velocimetry
Bibliographic record
Abstract
The development of atherosclerosis at the carotid bifurcation is impacted by local variations in wall shear stress (WSS) magnitude and direction, as well as flow complexity within the vessel. In this study, stereoscopic particle image velocimetry (PIV) was used to investigate multidirectional WSS and disturbed flow for idealized models of the carotid bifurcation with varying eccentric stenosis of the internal carotid artery (ICA) and both Newtonian (N-fluid) and non-Newtonian (nN-fluid) blood analogues. Turbulence intensity (TI) was reduced with the nN-fluid compared to N-fluid for mild to moderate stenosis, and comparable for more severely stenosed (70%) models. Differences in maximum TI due to viscosity model ranged from 0.02 m/s to 0.06 m/s compared to much larger differences due to geometry of up to 0.29 m/s between mild and severe stenosis. The level of time-averaged WSS (TAWSS) increased with stenosis severity from 5 Pa to 32 Pa, and nN-fluid led to higher WSS on average than N-fluid counterparts. Regions of elevated oscillatory shear index (OSI) demarcated recirculation regions, and mean OSI in the ICA branch was reduced for nN-fluid models by 9-19% compared to N-fluid. Transverse WSS (transWSS) increased with WSS magnitude and again was higher in nN-fluid models. Surface area exposure to shear metrics indicated that a Newtonian viscosity assumption predicted larger regions of low and oscillatory WSS, while predicting reduced regions of high transWSS, in comparison to the more physiological shear thinning fluid.
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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) | 0.000 | 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 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".