Numerical analysis of Dielectric Barrier Discharge Plasma Actuators for supersonic flow applications
Bibliographic record
Abstract
Surface Dielectric Barrier Discharge Plasma Actuator (SDBD PA) is a device that allows for a flow control near a surface of an object.In its simplest form the device consists of one electrode exposed to the atmosphere, dielectric and encapsulated grounded electrode.The device generates strong electric due to high voltage applied to the exposed electrode.Under the effect of the strong electric field the gas in the atmosphere is ionized and turns into plasma, which is manipulated by changing the electric field.The energy from the plasma is transferred to the surrounding gas through particle collisions allowing for the airflow control.The current work is concentrated on the supersonic applications of SDBD PA and aims at answering two questions:• What effects does supersonic shock have on plasma generated by SDBD PA?• What effects does SDBD PA have on supersonic shock?To answer these questions an OpenFOAM solver was developed that allows to simulate plasma within a supersonic flow.The solver is based on electron-positive nitrogen ions drift-diffusion model for plasma.Several simulations are performed to observe the plasma behaviour: plasma propagation through a frozen shock along a flat plate, supersonic flow under free-slip and no-slip conditions around a wedge with SDBD PA at the tip of the wedge.The Mach number varied from 1.3 to 2, depending on the simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".