Active Flow Control for Reduction of Fluctuating Aerodynamic Forces of a Blunt Trailing Edge Airfoil
Bibliographic record
Abstract
Vortex shedding from the base of two dimensional bluff bodies is accompanied by three dimensional wake instabilities. These instabilities manifest as streamwise and vertical vorticity components which occur at a certain spanwise wavelength. The spanwise wavelength of the instabilities (λz) depends on several parameters, including profile geometry and Reynolds number. The present study aims to determine λz for a blunt trailing edge airfoil, which is comprised of an elliptical leading edge, followed by a rectangular section. Results of numerical simulations of flow around the airfoil at Re(d) = 500, 800, 1200, and 17,000, and flow visualization at Re(d) = 2200 indicate that λz has an average value of 2.2d. An active flow control mechanism based on the three dimensional wake instabilities is proposed, to attenuate the fluctuating aerodynamic forces of the airfoil. The mechanism is comprised of trailing edge injection ports distributed across the span, with a spacing equal to λz. Injection of a secondary flow leads to excitation of the three dimensional instabilities and disorganization of the von Ka´rma´n vortex street. Numerical simulations at Re(d) = 500 and 17,000 indicate that the flow control mechanism can attenuate the fluctuating aerodynamic forces significantly, and reduce mean drag using a relatively small injection mass flow rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".