Development of Large-Scale Particle Tracking Velocimetry: A Comparison Between 2D and 3D Techniques
Bibliographic record
Abstract
This study compares the three-dimensional version of a previously proposed large-scale particle tracking velocimetry (LS-PTV) measurement system with a simpler, less computationally expensive, two-dimensional version for use in in-situ measurements. The LS-PTV (3D) system was used as a baseline for measuring wind flow over a flat roof in a 3m x 3m x 1.5m volume. The LS-PTV (2D) system covered a 1.5m x 3m plane within the same volume. A 3D ultrasonic anemometer was used outside of the measurement volume as a means to validate the LS-PTV measurements. An analytical model of the bubbles used as tracer particles for the LS-PTV measurements determined that the bubbles were able to resolve fluid length-scales 0.25m or larger, which was within the size of the measurement volume. Despite being more susceptible to errors due to incomplete background image subtraction, which was responsible for noise in the tracking algorithm, the LS-PTV (2D) system was able to track approximately 80% more bubbles and at a lower spatial uncertainty than the LS-PTV (3D) system. The LS-PTV (2D) system was unable to measure an exact projection of the LS-PTV (3D) results, which was a result of the LS-PTV systems tracking different bubbles within the same flow field. Both LS-PTV systems were found to measure the same velocities and Reynolds stresses within the field, and the missing vertical component from the LS-PTV (2D) measurements was deemed negligible.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".