MODELING BLOOD FLOW IN AN ECCENTRIC STENOSED ARTERY USING LARGE EDDY SIMULATION AND PARALLEL COMPUTING
Bibliographic record
Abstract
Computational fluid dynamics (CFD) is an excellent computational tool to assess the hemodynamics and detailed blood-flow structure for cardiovascular applications. Modeling turbulence for cardiovascular applications can be achieved (to some extent) using available numerical models such as Reynolds average Navier–Stokes (RANS), the large eddy simulation (LES) and the direct numerical solution (DNS). In order to develop an efficient model which is as accurate as DNS and as quick as RANS, our laboratory's focus is on LES. In this study, we develop an efficient numerical model which is based on LES and structured but non-orthogonal finite volumes. Using the proposed model, the detailed flow structure and turbulent features of the blood stream in a complicated geometry is captured. The aim of this study is to model blood-flow through an eccentric stenosis accurately and quickly. The results are similar to those obtained using DNS but in a fraction of the CPU time. The computational tools implemented in this study are based on a FORTRAN based in-house code coupled with parallel computing using SHARCNET. The developed model is a significant computational tool which can be used to assess the hemodynamic properties for cardiovascular applications, e.g., prosthetic heart valves and atherosclerosis.
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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) | 0.000 | 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".