Controlling Corner Stall Separation With Plasma Actuators in a Compressor Cascade
Bibliographic record
Abstract
This paper presents a numerical and experimental assessment of a plasma actuation concept for controlling corner stall separation in a highly loaded compressor cascade. CFD simulations were first carried out to assess actuator effectiveness and determine the best actuation parameters. Subsequently, experiments were performed to demonstrate the concept and confirmed the CFD tool validity at a Reynolds number of 1.5 × 105. Finally, the validated CFD tool was used to simulate the concept at higher velocities, beyond the experimental capability of existing plasma actuators. These results were used to obtain a preliminary scaling law that would allow approximation of the plasma actuation requirements at realistic operating conditions. Several configurations were examined, but the most effective setup was found to be when plasma actuators were mounted upstream of the separation point on both the suction surface and the endwall. Most of the improvement in total pressure loss stemmed from the suction surface actuator. Comparison with experimental data showed that the CFD simulations could capture the flow features and the effect of plasma actuation reasonably well. Simulations at higher flow velocities indicated that the required plasma actuator strength scales approximately with the square of the Reynolds number.
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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 | 0.001 |
| 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".