Turbulent Boundary Layer Separation Control by Using DBD Plasma Actuators: Part II—Numerical Model Validation and Parametric Study
Bibliographic record
Abstract
Following an experimental investigation into suppression of a 2-D turbulent boundary layer separation with dielectric barrier discharge (DBD) plasma actuators, the present work investigates the concept numerically. The purpose is to develop and validate a simulation tool that captures the flow physics and carry out a parametric study of the concept at flow regimes beyond the current flow control capability of plasma actuators of conventional strength. First, a plasma actuator model is integrated into the commercial computational fluid dynamics (CFD) code ANSYS CFX to simulate the effects of plasma actuation. This computational tool is validated through comparison of results with the experimental results for pulsed actuation in quiescent air and for the control of a turbulent boundary layer separation at low flow velocities. It is shown that CFX with an integrated plasma model can capture the main experimentally observed effects of DBD actuators on turbulent boundary layer separation. Subsequently, this numerical approach is used, with increased plasma actuator strength, to study the influence of different actuation parameters (e.g., actuation location, direction and frequency) on suppression of turbulent boundary layer separation at higher flow velocities.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".