Investigation of Unsteady Wake-Separated Boundary Layer Interaction Using Particle-Image-Velocimetry
Bibliographic record
Abstract
The current paper presents an experimental investigation of the interaction between unsteady wakes and the separated boundary layer on the suction side of an ultra-high-lift low-pressure turbine airfoil. Two-dimensional Particle Image Velocimetry (PIV) measurements of the unsteady boundary layer over the T106C LP turbine profile were performed in a low speed linear cascade facility, at selected phases of passing wakes. The wakes are created by moving cylindrical bars across the inlet of the test section. Various phenomena were investigated such as separation and transition characteristics, vortex structures within the unsteady boundary layer, their interaction and effects on the transition process, the corresponding vortex shedding mechanisms and the unsteady behaviour of the separation bubble due to the wake- boundary layer interaction. The current measurements suggest that rollup vortices are generated as the wake approaches the separated shear layer on the suction surface before the wake centerline starts impinging on the blade. At this instant, the bubble is sufficiently high for the free shear layer to roll up into a vortex and the incoming wake is highly distorted (strained) due to the velocity field within the blade passage, and the turbulence distribution within the wake is not symmetrical. Vortices within the boundary layer, identified using the swirl strength distributions calculated from the eigenvalues of the velocity gradient tensor, seem to be coalescing and forming bigger scale structures, which in turn break up into smaller but higher swirl strength eddies. In between the passing wakes, the separation bubble grows in both in height and length, trying to return to its steady state shape.
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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 | 0.000 | 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".