On the use of helium-filled soap bubbles for large-scale tomographic PIV in wind tunnel experiments
Bibliographic record
Abstract
The flow-tracing fidelity of sub-millimetre diameter helium-filled soap bubbles (HFSB) for low-speed aerodynamics is studied. The main interest of using HFSB in relation to micron-size droplets is the large amount of scattered light, enabling larger-scale three-dimensional experiments by tomographic PIV. The assessment of aerodynamic behaviour closely follows the method proposed in the early work of Kerho and Bragg (Exp Fluids 50:929–948, 1994) who evaluated the tracer trajectories around the stagnation region at the leading edge of an airfoil. The conclusions of the latter investigation differ from the present work, which concludes sub-millimetre HFSB do represent a valid alternative for quantitative velocimetry in wind tunnel aerodynamic experiments. The flow stagnating ahead of a circular cylinder of 25 mm diameter is considered at speeds up to 30 m/s. The tracers are injected in the free-stream and high-speed PIV, and PTV are used to obtain the velocity field distribution. A qualitative assessment based on streamlines is followed by acceleration and slip velocity measurements using PIV experiments with fog droplets as a term of reference. The tracing fidelity is controlled by the flow rates of helium, liquid soap and air in HFSB production. A characteristic time response, defined as the ratio of slip velocity and the fluid acceleration, is obtained. The feasibility of performing time-resolved tomographic PIV measurements over large volumes in aerodynamic wind tunnels is also studied. The flow past a 5-cm-diameter cylinder is measured over a volume of 20 × 20 × 12 cm3 at a rate of 2 kHz. The achieved seeding density of <0.01 ppp enables resolving the Kármán vortices, whereas turbulent sub-structures cannot be captured.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".