Application of a Particle Image Velocimetry System to the Investigation of Unsteady Transonic Flows in Turbomachinery
Bibliographic record
Abstract
In the present paper a study of the time dependent transonic flow field around a single compressor blade in a Laval nozzle is presented.For the investigation the PIV method has been employed because of its ability to deliver instantaneous flow field information across an illuminated plane with high spatial and temporal resolution.From these instantaneous PIV measurements the mean velocity field and turbulence quantities of the flow can be easily obtained by statistical means.An isolated Plexiglas compressor blade is mounted in the test section of the Laval nozzle.This blade can be vibrated by an hydraulic excitation system in a controlled plunging mode.In addition, the exit pressure level of the nozzle can be varied periodically by a rotating flat plate.The above excitation systems can be precisely synchronized and the phase lag between them can be freely varied.This allows for unsteady measurements to be conducted in the presence of only the downstream perturbation, only the blade vibration, or a combination of the two for different phase angles.For the first test series, the PIV system was used to measure the steady flow field around the compressor blade.In another test series, measurements of the time dependent periodic flow field were conducted by means of PIV.These results quantify the unsteady motion of the normal shock on the suction side of the blade.Finally, phase averaging of the instantaneous flow quantities yield a big database for statistical treatment (e.g.turbulence) and the ability to compare the averaged results with traditional measurement techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".