Commissioning a Stereoscopic Particle Image Velocimetry System for Application in a Towing Tank
Bibliographic record
Abstract
In January 2004, Memorial University purchased a stereoscopic Particle Image Velocimetry (PIV) system for making flow measurements. Although the system was potentially very versatile, its primary application was envisioned to be in a towing tank. The system was supplied as a complete package of hardware and software for calibration, data collection and analysis. The intention of the PIV system designers was to collect as many data frames as possible for a fixed measurement plane location, relative to the model. This required the PIV system to be fixed on the towing carriage, moving with the model being studied. It was found during some preliminary experiments that an active seeding delivery system was necessary to ensure sufficiently high seeding particle concentration, especially when measuring flow velocities around a model hull with a large yaw angle. This paper describes the development of a seeding technique for a PIV system that can be used in a towing tank. The paper also includes an estimate of the uncertainty of the measurement system, including the analysis of the flow behind the device used to deliver seed particles into the flow.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".