Distributed forcing flow control in the wake of a blunt trailing edge profiled body using plasma actuators
Bibliographic record
Abstract
A modern flow control technique for reducing the drag associated with the periodic shedding of von Kármán vortices in the wake of a blunt trailing edge profiled body is presented. The technique involves distributed forcing of the wake flow using an array of dielectric barrier discharge plasma actuators, with a spanwise spacing matched to the spanwise wavelength of the dominant secondary wake instability. The experiments include measurement of the velocity field in multiple vertical and horizontal planes in the wake using particle image velocimetry, as well as base pressure, at Reynolds numbers of 2000, 3000, and 5000 based on trailing edge thickness. The flow control technique causes elongation of the vortex formation region across the span, and significant reduction of the fluctuating and total drag forces, up to a maximum of 94% and 18%, respectively. The effectiveness of the flow control technique is shown to be dependent on the induced momentum coefficient. Proper orthogonal decomposition analysis is used to investigate the mechanism of interaction of the flow control technique with the wake flow. Two distinct flow regimes are observed depending on the induced momentum coefficient. The effect of the control on the wake flow structure in the first regime is similar to those observed in previous studies involving mild spanwise-periodic geometric perturbations at the trailing edge, where control leads to streamwise displacement of the vortices and a shift in shedding frequency. However, an incremental increase in the momentum coefficient leads to a second flow regime similar to those previously observed in the case of large-amplitude geometric perturbations, with an almost complete attenuation of vortex shedding in the near-wake region.
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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) | 0.000 | 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".