Steady-State Closed-Loop Control of Bypass Boundary Layer Transition Using Plasma Actuators
Bibliographic record
Abstract
The overarching objective motivating this work is a physical demonstration of modelbased, closed-loop control of bypass transition using plasma actuators. The present work is concerned with the closed-loop control of bypass transition using plasma actuators. This manuscripts extends the work by Hanson et al., 1,2 who demonstrated that a spanwise array of plasma actuators can produce significant attenuation of the transient growth disturbances introduced by roughness elements. In the present work, the control loop is closed based on feedback from wall-shear stress measurements. The control signal is based on empirical modelling of the input/output flow response for several flow conditions. The latter is obtained for both the main disturbance, generated by a roughness-element array, as well as the control disturbance, forced using a spanwise plasma actuator array. The output is characterized using wall-shear-stress measurements downstream of the actuation location. The controller is designed to minimize the residual disturbance energy in the output measurements at the target instability spanwise wavenumber. The control model developed in this work was applied to three steady disturbance cases, including one that is outside of the parameter range for which the input/output model was developed. The closed-loop control model is shown to effectively attenuate the boundary layer disturbance by 1,end > 95% in each case, with the initial control iteration accounting for 1,1 > 89%.
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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 | 0.001 | 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".