Active Flow Control Using Steady Blowing for a Low-Pressure Turbine Cascade
Bibliographic record
Abstract
The paper presents mid-span measurements for a turbine cascade with active flow control. Steady blowing through an inclined plane wall jet has been used to control the separation characteristics of a high-lift low-pressure turbine airfoil at low Reynolds numbers. Measurements were made at design incidence for blowing ratios from approximately 0.25 to 2.0 (ratio of jet-to-local freestream velocity), for Reynolds numbers of 25,000 and 50,000 (based on axial chord and inlet velocity), and for freestream turbulence intensities of 0.4% and 4%. Detailed flow field measurements were made downstream of the cascade using a three-hole pressure probe, static pressure distributions were measured on the airfoil suction surface, and hot-wire measurements were made to characterize the interaction between the wall jet and boundary layer. The primary focus of the study is on the low-Reynolds number and low-freestream turbulence intensity cases, where the baseline airfoil stalls and high profile losses result. For low freestream turbulence (0.4%), the examined method of flow control was effective at preventing stall and reducing the profile losses. At a Reynolds number of 25,000, a blowing ratio greater than 1.0 was required to suppress stall. At a Reynolds number of 50,000, a closed separation bubble formed at a very low blowing ratio (0.25) resulting in a significant reduction in the profile loss. For high freestream turbulence intensity (4%), where the baseline airfoil has a closed separation bubble and low profile losses, blowing ratios below 1.0 resulted in a larger separation bubble and higher losses. The mechanism by which the wall jet affects the separation characteristics of the airfoil is examined through hot-wire traverse measurements in the vicinity of the slot.
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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 | 0.001 |
| 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 | 0.001 |
| 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".