Low Reynolds Number Flow Control Over an Airfoil Using Synthetic Jet Actuators
Bibliographic record
Abstract
The influence of periodic excitation from synthetic jet actuators, SJA, on boundary layer separation and reattachment over a NACA 0025 airfoil at a low Reynolds number is studied. All experiments were performed in a low-turbulence recirculating wind tunnel at a Reynolds number of 100000 and angle of attack of α = 0°. Mounted just below the surface of the airfoil, the SJA consists of four (32.77mm diameter) piezo-electric ceramic diaphragms positioned in a single row. Flow visualization and hot wire tests were conducted with the SJA outside of the airfoil to characterize the exit flow. Results from flow visualization show a vertical jet pulse accompanied by two counter rotating vortices being produced at the exit of the simulated slot, with the vortices shed at the excitation frequency. Based on flow visualization results, the length scales of successive vortices were used to estimate the exit velocities. Hot-wire measurements determined the maximum jet velocity for a range of excitation frequencies (f = 50Hz–2.7kHz) and voltages (Vp–p = 50V–300V), which was used to characterize the excitation amplitude in terms of the momentum coefficient (cμ). With the SJA installed in the airfoil, preliminary flow visualization results show a reattachment of the boundary layer and a significant reduction in wake width.
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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 | 0.000 |
| 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".