Aerodynamics of a Low-Pressure Turbine Airfoil at Low-Reynolds Numbers: Part 2 — Blade-Wake Interaction
Bibliographic record
Abstract
The relative motion of rotor and stator blade rows causes periodically unsteady flows that influence the performance of airfoils through their effects on the boundary layer development. Part 1 of this two-part paper described the influence of Reynolds number, freestream turbulence intensity and turbulence length scales on a low-pressure (LP) high-lift turbine airfoil, PakB, under steady inlet flow conditions. The aerodynamic behaviour of the same airfoil under the influence of incoming wakes is presented in Part 2. The unsteady effects of wakes from a single upstream blade-row were measured in a low-speed linear cascade facility at Reynolds numbers of 25000, 50000 and 100000 and at two freestream turbulence intensity levels of 0.4% and 4%. In addition, eight reduced frequencies between 0.53 and 3.2, at three flow coefficients of 0.5, 0.7 and 1.0 were examined. The complex wake-induced transition, flow separation and reattachment on the suction surface boundary layer was determined from an array of closely-spaced surface hot-film sensors. The wake-induced transition caused the separated boundary layer to reattach to the suction surface at all conditions examined. The time-varying profile losses were measured downstream of the trailing edge. Profile losses increase with decreasing Reynolds number and the influence of increased freestream turbulence intensity is only evident in between wake-passing events at low reduced frequencies. At higher values of reduced frequency, the losses increase slightly and for the cases examined here, losses were slightly larger at lower flow coefficients than the higher flow coefficients. An optimum wake-passing frequency was observed at which the profile losses were a minimum.
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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 | 0.001 | 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".