Manipulation of a turbulent boundary layer using active surface deformations
Bibliographic record
Abstract
We experimentally evaluate whether active wall-normal surface deformations are suitable for the targeted control of very-large-scale motions (VLSMs) in a turbulent boundary layer at a friction Reynolds number of $Re_\tau =2600$ . Circular surface deformations with a diameter $D$ roughly equal to the boundary layer thickness $\delta$ are generated periodically at a constant amplitude of $0.03\delta$ and at actuation frequencies of $St=0.05$ to 0.20, where $St$ is the Strouhal number based on $D$ and the free stream velocity $U_\infty$ . The resulting impact on the flow was captured using high-speed particle image velocimetry and analysed using a triple decomposition. We find that the active surface deformations produce high- and low-speed streamwise velocity fluctuations that are concentrated along the centreline of the actuator. These motions have a negligible impact on the mean velocity profile downstream, i.e. they are truly high and low speed with respect to the unactuated base flow. The motions produced at $St\lesssim 0.1$ are comparable to synthetic VLSMs in terms of their lengths and widths but with a reduced wall-normal extent and rapidly decaying strength. These synthetic motions produce a strong modulation of the turbulence similar to that of the naturally occurring VLSMs. Most notably, we observe that synthetic high-speed motions with magnitudes of the order of $0.05U_\infty$ cause up to a 30 % reduction in turbulence production within the logarithmic layer. The strength and turbulence-modulating characteristics of the synthetic motions appear well suited for targeting the naturally occurring VLSMs locally using a control scheme.
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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) | 0.001 | 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".