Impact of Wall Property and Flow Rate Assumptions on Simulations of Flow‐Induced Vibration of Intracranial Aneurysms
Bibliographic record
Abstract
ABSTRACT Recent high‐fidelity fluid–structure interaction (FSI) simulations of cerebral aneurysms have revealed flow‐induced wall vibrations. However, those simulations were conducted under simplified conditions, and the robustness of the predicted vibrations remains unknown. This study aimed to advance the physiological accuracy of previous models and to investigate the sensitivity to parameter uncertainty. We compared the previously used near‐linear St. Venant–Kirchhoff wall model with a three‐term hyperelastic Mooney–Rivlin (MR3) model fitted to experimental data and also modeled effects of surrounding cerebrospinal fluid (CSF). We then varied flow rate (1.83 mL/s 25%), wall stiffness (soft, medium, stiff), and wall thickness (0.25 0.1 mm). Our main findings for the four aneurysms considered were as follows: the MR3 model led to an average increase of 35% in pulsation and 240% in vibration amplitude, along with an 18% decrease in frequency. Viscous damping by the CSF reduced the vibration amplitude by 68% but did not affect the frequency or pulsation. Changes in flow rate had no effect on pulsation but increased vibration amplitude by 246%. Wall stiffness and thickness had a comparatively smaller impact on vibration, altering amplitude by 36% and 82% and frequency by 20% and 8%. In conclusion, the more advanced models led to a decrease of vibration amplitude and frequency during the cardiac cycle, consistent with clinical observations. Like computational fluid dynamics, FSI simulations can be sensitive to flow rates but are otherwise robust and can provide a fundamental understanding of aneurysm wall vibration without precise knowledge of wall properties.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".