Scaling of maximum velocity, body force, and power consumption of dielectric barrier discharge plasma actuators via particle image velocimetry
Bibliographic record
Abstract
This study presents Particle Image Velocimetry (PIV) measurements of the induced flow characteristics generated by single dielectric barrier discharge (DBD) actuators in quiescent conditions. The primary aim is to establish accurate empirical trends for model development on both the maximum induced velocity and body force with voltage and consumed power. The results reveal a power law variation for the maximum velocity at low voltages which is followed by an asymptotic behavior. In contrast, the body force is characterized by two power law regions. The power law exponent is shown to be a function of the dielectric thickness, frequency and dielectric constant. Reducing the former or increasing the latter two result in a higher coefficient and lower voltage at which the trend changes. The onset of the second region occurs at a Re ∼ 100 (based on the maximum velocity, um, and corresponding half height, y1/2) and is characterized by a velocity profile which no longer agrees with the laminar profile of Glauert whilst moving increasingly towards the turbulent case. Phase locked PIV measurements show that as the voltage increases the peak momentum transfer shifts from the middle of the AC cycle to the latter end of the forward stroke. Lissajous plots of umϕ against the corresponding x location and plasma length Δx demonstrate that the peak momentum transfer remains relatively fixed in space as the voltage and plasma length increase.
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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) | 0.000 | 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".