Turbulent Boundary Layer Separation Control by Using DBD Plasma Actuators: Part I—Experimental Investigation
Bibliographic record
Abstract
Turbulent boundary layer separation is an important issue for a variety of applications, one of which is S-shaped aircraft engine intakes. The turbulent separation at the engine intake causes inlet flow distortion, which can deteriorate engine performance, cause fatigue and reduce engine component life. Various flow control techniques have been applied for turbulent boundary layer separation control, such as vortex generators, vortex generator jets and synthetic jets. The recent advent of dielectric barrier discharge (DBD) plasma actuators can potentially provide a robust method for the control of turbulent boundary layer separation. Compared to other flow control techniques, these new actuators are simple, robust and devoid of moving mechanical parts, which make them ideal for aerodynamic applications. The present work studies the effects of DBD plasma actuators on the suppression of 2-D turbulent boundary layer separation induced by an imposed adverse pressure gradient. First, the flow field with and without actuation in a low-speed wind tunnel is investigated experimentally by Particle Image Velocimetry (PIV) measurements. The results show that plasma actuation can suppress turbulent boundary layer separation in both continuous and pulsed modes. In the pulsed mode, the actuation with an optimal actuation frequency, corresponding to a dimensionless frequency of order one, is found to most effectively suppress the turbulent separation. Moreover, the effects of plasma actuation on the flow is demonstrated and analyzed by using Proper Orthogonal Decomposition (POD). The effect of the actuation is found to be correlated to the second POD mode which corresponds to large flow fluctuations.
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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".