CFD Analysis of Particle Dynamics in Accelerated Toroidal Systems for Enhanced PIVG Performance
Bibliographic record
Abstract
This study investigates the movements of particles in an accelerated toroidal flow channel filled with water, with specific applications for a particle imaging velocimetry gyroscope (PIVG). We used computational fluid dynamics (CFD) to simulate particle behavior under different angular accelerations. These angular accelerations were 4 rad/s2, 6 rad/s2, and 8 rad/s2 for particles densities of 1100 kg/m3, 1050 kg/m3, and 980 kg/m3. An examination was performed on the particles’ concentration distribution, velocity profiles, and displacement patterns with respect to the toroidal geometry, which had a volume fraction of 1.5% and was sized at 50 microns. Our results show that particle density significantly affects behavior and displacement within the toroidal flow, with heavier particles (1100 kg/m3) settling more quickly and concentrating near the lower z values over time, while lighter particles (980 kg/m3) maintain a more uniform distribution. This understanding is crucial for optimizing PIVG accuracy and reliability.
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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 | 0.000 | 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 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".