Numerical Study of the Effect of Corona Discharge on Upward Wake Flow in the Horizontal Axis Wind Turbine Farm
Bibliographic record
Abstract
Many countries worldwide are showing a growing interest in renewable energy sources, with wind energy being a particularly appealing option for generating mechanical energy. Researchers have explored different techniques for controlling the flow of air, including passive, active, and semi-active methods. In wind farms, the wake flow behind a turbine can be impacted by the flow from other turbines, and to address this issue, plasma-based corona discharge actuators are being considered as one of the most effective methods for reducing fluid flow separation on wind turbine blades. This study employs 2D and 3D numerical simulations to examine the use of corona discharge-based plasma actuators on the leading edge of tandem wind turbines within a wind farm. The study investigates how actuator voltage and frequency affect aerodynamic parameters such as lift, drag coefficients, and efficiency. The study incorporates the use of the Q-criterion to analyze vortex behavior and its interaction with the axial wind turbine body. Fluid flow modeling is conducted using the OPENFOAM software. The findings demonstrate that an escalation in both voltage and frequency of the corona discharge results in a decrease in the Q-criterion, attributed to the heightened ionic flow that diminishes the separation zone. Furthermore, reducing the distance between electrodes also aids in diminishing the Q-criterion values. Additionally, the study reveals that integrating corona plasma at the leading edge of wind turbine blades amplified power generation by more than 3.8%. The corona plasma actuator employed in the study had electrodes spaced 3 mm apart, operated at a voltage of 17 KV, and ran at a frequency of 13 kHz.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".