Finite-Element Modeling of the Hemodynamics of Stented Aneurysms
Bibliographic record
Abstract
BACKGROUND: Computational fluid dynamics (CFD) simulations are used to analyze the wall shear stress distribution and flow streamlines near the throat of a stented basilar side-wall aneurysm. Previous studies of stented aneurysm flows used low mesh resolution, did not include mesh convergence analyses, and depended upon conformal meshing techniques that apply only to very artificial stent geometries. METHOD OF APPROACH: We utilize general-purpose computer assisted design and unstructured mesh generation tools that apply in principle to stents and vasculature of arbitrary complexity. A mesh convergence analysis for stented steady flow is performed, varying node spacing near the stent. Physiologically realistic pulsatile simulations are then performed using the converged mesh. RESULTS: Artifact-free resolution of the wall shear stress field on stent wires requires a node spacing of approximately 1/3 wire radius. Large-scale flow features tied to the velocity field are, however, captured at coarser resolution (nodes spaced by about one wire radius or more). CONCLUSIONS: Results are consistent with previous work, but our methods yield more detailed insights into the complex flow dynamics. However, routine applications of CFD to anatomically realistic cases still depend upon further development of dedicated algorithms, most crucially to handle geometry definition and mesh generation for complicated stent deployments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".