Suppression of fluctuating lift on a cylinder via evolutionary algorithms: Control with interfering small cylinder
Bibliographic record
Abstract
A generalized model-free method to optimize parameters for open-loop and closed-loop control in fluid mechanics applications is presented. A multi-objective evolutionary algorithm (MOEA) is employed to minimize the oscillating lift caused by vortex shedding from a cylinder of diameter D via the insertion of a secondary control cylinder of diameter D/8. The first objective of the algorithm is to minimize the fluctuating force coefficient CLRMS, while the second objective is to minimize the actuation power required to drive the control cylinder. Experiments are carried out in a free surface water channel at ReD = 12 500 and verified for robustness to changes in Reynolds number at ReD = 17 000. The control cylinder is prescribed a position as well as a periodic sinusoidal motion in two dimensions. The MOEA efficiently handles the larger optimization parameter space, with the final solution suppressing CLRMS by over 90% using near-zero actuation power. Further, the MOEA inherently provides a sensitivity study as to the influence of the different parameters and also in which spatial area the greatest influence is expressed. The dynamics of the optimal suppression case are compared to those of the baseline case (no control cylinder) using phase averaged and mean particle image velocimetry and direct force measurements.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".