Precision Airflow Control via EHD Actuator: A Co-Simulation and Control Design Case Study
Bibliographic record
Abstract
A dielectric barrier discharge (DBD) plasma actuator for controlling airflow is proposed. It consists of diverging and converging nozzles, two concentric cylinders, and an actuator mounted in between the two cylinders. The actuator employs electrohydrodynamic (EHD) body force to induce an air jet within the air gap between the two cylinders, effectively creating a suction area while passing through the diverging nozzle, due to the Coanda effect. While merging with the air stream inside the inner cylinder, the Coanda jet effectively enhances the amplification of the airflow. The outflow rate is measured by a velocity sensor at the outlet and controlled by the plasma actuator. The control strategy is based on the active disturbance rejection control (ADRC) and compared to the baseline PID controller. The actuator was modeled by seamlessly linking two modeling platforms for a co-simulation study. The computational fluid dynamic (CFD) simulation of the plasma and airflow was carried out in the COMSOL multiphysics commercial software, and the control was implemented in Simulink. The DBD plasma model was based on the two-species model of discharge, and the electric body force, calculated from the plasma simulation, was used in the Navier-Stokes equation (NS) for the turbulent flow simulation using$k-\omega $model. The plasma-airflow system was analyzed using the input (the actuator voltage) and output (the outlet flow rate) data for the control design. Finally, the performance of the system of airflow control device was tested and discussed in the co-simulation process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".