Decomposition of blood flow in a cerebral artery with an aneurysm
Bibliographic record
Abstract
Brain aneurysms occur when the wall of a blood vessel weakens and expands. The rupture of a brain aneurysm has devastating effects. However, the precise causes of this disease are still unknown, although it is believed that blood flow plays a key role. The flow within aneurysms is complex, difficult to measure and interpret, with more studies needed. The purpose of the present study is therefore to evaluate the decomposition of blood flow within aneurysms, to improve our understanding and potentially help separate pathological from physiological flow patterns. Direct numerical computational fluid dynamics simulations, using OpenFOAM, are evaluated using spectral proper orthogonal decomposition (SPOD) and triple decomposition, or phase-averaging, techniques. The velocity and the wall shear stress fields are decomposed. Phase-averaging is used to separate the base pulsatile and physiological laminar flow from the turbulent fluctuations, while the SPOD is used to identify the most energetic space–time coherent structures in the flow. The results obtained from the decomposition techniques are promising, in particular with the SPOD identifying a significant frequency peak around 25 Hz in the realistic aneurysm geometry studied here. In addition, two main vortical structures are identified in the mean flow. Decomposition assessments such as the ones performed in this study can have important consequences in the evaluation of aneurysm pathophysiology, considering that vascular walls may be affected differently depending on the flow structure and characteristic frequencies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".