Feed-forward Control of Vortices Using Real-Time Particle Image Velocimetry
Bibliographic record
Abstract
Active flow control (AFC) is a topic with enormous potential for improving a wide array of technologies. This work presents the use and evaluation of a novel AFC system to apply feed-forward control to vortices formed in the wake of a wall-mounted spherical cap. A real-time particle image velocimetry (RT-PIV) system served as the sensor for the AFC system. The RT-PIV system produced 7.35 vector fields per second, each consisting of a two-dimensional grid of 5 × 37 velocity vectors. The actuator system consisted of a rubber surface that could deform using 16 independently controlled linear actuators. The actuators were controlled to apply wall-normal surface deformation at velocities proportional to wall-normal velocities measured by the RT-PIV system upstream of each of the 16 actuators. Analysis of the RT-PIV measurements in comparison to those from an offline PIV system run in parallel indicated that the RT-PIV system produced accurate flow measurements. As well, it was found that the RT-PIV system had a delay of approximately 0.09 seconds. The evaluation of the actuator system indicated that the actuator displacements were consistently damped by 20-25% and lagged the control signal by approximately 0.1 seconds relative to the input signal to the actuators. Therefore, the AFC system had a total delay of approximately 0.2 seconds and applied slightly weaker than specified actuations. These results were within anticipated values and, overall, the AFC system functioned adequately. The impacts of the flow control included disruption of the coherent ejection and sweep structures formed in the wake of the spherical cap and the formation of numerous smaller turbulent structures. As well, several of the control cases showed altered wall-normal velocity fluctuation variance fields in comparison to that of the unforced wake. This is a promising result for AFC using deformable surface actuators as it shows that the active control had a significant effect on an average quantity of the flow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".