Simultaneous Cinematographic PIV and Acetone PLIF for Spatially and Temporally Resolved Velocity and Concentration Fields in a Buoyant Helium Plume
Bibliographic record
Abstract
Abstract An experimental study has been performed on a 1-m base diameter buoyant helium plume. Velocity fields were measured by particle image velocimetry (PIV). Helium concentration was determined by seeding the helium with 1.7-volume percent acetone vapor and measuring planar laser-induced fluorescence (PLIF) of the acetone. PIV and PLIF were performed simultaneously using a 200 Hz XeCl excimer laser and 35-mm motion picture cameras. The film images were digitized and post-processed to obtain velocity and concentration data for a region approximately 0.8 m high by 1 m wide centered on the plume centerline and extending from the surface of the plume inlet to include the pure helium core, near-field mixing zones, and surrounding air. The data cover 7 puff cycles of the plume. Instantaneous and time-averaged two-dimensional velocity fields were obtained for each of 900 time planes (129 time-planes per puff cycle) spaced 5 ms apart. Each vector represents a statistical estimate of the velocity in a 2.1 cm by 2.1 cm by 0.8 cm volume, which are overlapped by 50% in the vector plots. Time-averaged turbulent statistics ( u′ 2 ¯ , v′ 2 ¯ , u′ v′ ¯ , and k) are also presented. The joint velocity and concentration data are used to calculate Favre-averaged statistics. Boundary conditions were carefully measured as the data are intended for development and validation of numerical simulations of buoyant turbulence using RANS and LES techniques. The results clearly show the dominant effect of puffing, measured at 1.55 cycles/sec for this plume, on the temporal and spatial development of the velocity field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".