Improving thrust by pulse-induced breakdown enhancement in AC surface dielectric barrier discharge actuators for airflow control
Bibliographic record
Abstract
Abstract The characteristics of a plate-to-plate AC surface dielectric barrier discharge (SDBD) actuator using the pulse-induced breakdown enhancing method are experimentally investigated. The encapsulated electrode is supplied with a sine high AC voltage, while the exposed electrode is feed by a synchronized pulse voltage. Based on the thrust force and power consumption measurements, a parametric study was performed using a positive pulse applied at the trough phase of the AC cycles in which the thrust force was observed to increase by about 100% to 300% and the efficiency up to about 100% compared with the AC-only supply conditions for different AC voltages within the tested range. The pulse-induced breakdown effect was analyzed from the electrical and light emission waveforms to reveal the underlying mechanism. The surface potential due to the charge deposition effect was also measured using a specially designed corona-like discharge potential probe. It is shown that the pulse-induced breakdown was able to cause a temporarily intensified local electric field to enhance the glow-like discharge and meanwhile increase the time-average surface potential in the region further downstream. The improvement in the force by the enhancement in the pulse-induced breakdown was mainly due to enhancements in the glow-like discharge and the surface potential increment, with the latter being more important when the AC voltage is higher.
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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".