Experimental and Numerical Investigation of Corner Stall in a Highly-Loaded Compressor Cascade
Bibliographic record
Abstract
Three-dimensional corner stall is one of the most important factors limiting compressor performance. This paper presents a complementary experimental and computational study of corner stall in a highly-loaded compressor cascade subjected to three inlet boundary layer thicknesses, two levels of freestream turbulence intensity and two Reynolds numbers. Experiments included seven-hole pressure probe traverses, airfoil loading and surface oil flow visualization. Measurements were supplemented with the numerical predictions from a commercially available CFD code. It was found that the low momentum boundary layer on the endwall was unable to overcome the large streamwise adverse pressure gradient in this high-lift profile and turned sharply towards the midspan due to the strong cross-passage pressure gradient. The corner stall, with distinct regions of three-dimensional reversed flow, started at 50% chord and occupied a large area of the suction surface as well as the downstream passage. Only a small region of the inlet boundary layer, very close to the endwall seemed to play a role in the corner stall. As such, the flow in the endwall region was found to be nearly independent of the inlet boundary layer thickness, freestream turbulence intensity and Reynolds number. Based on the endwall flow structures, a new topology of corner stall for compressor cascades with high airfoil diffusion factor and high flow turning has also been proposed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".