Suppression of Short Length-Scale Rotating Stall Inception With Glow Discharge Actuation
Bibliographic record
Abstract
This paper proposes and investigates the pioneering use of glow discharge (plasma) actuation to suppress short length-scale (spike) rotating stall inception. A single dielectric barrier discharge plasma actuator basically consists of two parallel offset thin electrodes separated by a dielectric material. The application of a high frequency AC voltage across the electrodes results in an induced body force on the flow adjacent to the surface. This simple, robust actuator may provide a practical low-power mean to positively alter the tip clearance flow dynamics responsible for spike stall inception. A computational study is carried out on a low-speed compressor rotor with the implementation of a published plasma actuation model in an established turbomachinery CFD code. The objective is to provide a preliminary assessment of the effectiveness of a casing circumferential plasma actuator, with varying actuator location, input voltage and frequency, in suppressing the two flow criteria associated with the formation of spike disturbances leading to stall. Results show that plasma actuation most effectively suppresses both of these flow criteria when placed near the rotor leading edge and delays the predicted stall point to a lower flow coefficient with minimal power input. The simulations also indicate that the effectiveness of the actuation decreases non-linearly with input voltage and frequency. In addition, results indicate that this technology could perhaps be used for suppression of both short and long-length scale stall inception in axial compressors.
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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) | 0.000 | 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".