On Assessing the Quality of Particle Tracking Through Computational Fluid Dynamic Models
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Bibliographic record
Abstract
Quantification of particle deposition patterns, transit times, and shear exposure is important for computational fluid dynamic (CFD) studies involving respiratory and arterial models. To numerically compute such path-dependent quantities, it is necessary to employ a Lagrangian approach where particles are tracked through a pre-computed velocity field. However, it is difficult to determine in advance whether a particular velocity field is sufficiently resolved for the purposes of tracking particles accurately. Towards this end, we propose the use of volumetric residence time (VRT)--previously defined for 2-D studies of platelet activation and here extended to more physiologically relevant 3-D models--as a means of quantifying whether a volume of Lagrangian fluid elements (LFE's) seeded uniformly and contiguously at the model inlet remains uniform throughout the flow domain. Such "Lagrangian mass conservation" is shown to be satisfied when VRT=1 throughout the model domain. To demonstrate this novel concept, we computed maps of VRT and particle deposition in 3-D steady flow models of a stenosed carotid bifurcation constructed with one adaptively refined and three nominally uniform finite element meshes of increasing element density. A key finding was that uniform VRT could not be achieved for even the most resolved meshes and densest LFE seeding, suggesting that care should be taken when extracting quantitative information about path-dependent quantities. The VRT maps were found to be useful for identifying regions of a mesh that were under-resolved for such Lagrangian studies, and for guiding the construction of more adequately resolved meshes.
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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 it