Reduction of Fan and Compressor Wake Defect Using Plasma Actuation for Tonal Noise Reduction
Bibliographic record
Abstract
This paper proposes a new technique to reduce the noise generated by rotor-stator interaction (tonal noise) in fans and compressors. The method involves the use of single dielectric barrier discharge (plasma) actuators near the blade trailing edge to reduce blade wakes. Plasma actuators are a new and simple type of active flow control device consisting of two parallel and offset electrodes separated by a layer of dielectric material. The application of a high ac voltage at high frequency to the electrodes generates a body force on the flow in the vicinity of the electrodes to inject momentum without mass addition. A preliminary assessment of the proposed concept is performed with a computational study on modern low-speed compressor rotor geometry. A plasma actuator model is implemented in an established turbomachinery CFD code. Simulations are carried out to evaluate the effect of the actuator strength, location, and actuation method (continuous versus pulsed) on the rotor wake. Results show that plasma actuators operated in continuous mode near the trailing edge can significantly influence the wake of the rotor with relatively little power consumption. The effectiveness of the actuation is proportional to actuator strength (induced body force). The exact position of the actuator in the trailing edge region has little effect on the effectiveness of the actuation. The results from simulations with pulsed actuation show very low time-averaged influence on the wake and are not fully conclusive, due possibly to the frequencies simulated and the limitations of the RANS CFD tool.
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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) | 0.001 | 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".