Closed‐loop adaptive model predictive control of a bluff body wake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A machine learning methodology is outlined to achieve robust closed‐loop feedback control of a bluff body turbulent wake. A Long Short‐Term Memory (LSTM) Neural Network is implemented with Model Predictive Control (MPC) to achieve closed‐loop flow control. The LSTM model is trained using actuation and pressure sensor data to forecast future pressure states. The candidate system is a square cross‐sectional cylinder with two modulated moving surface actuators embedded in the windward face leading corners. The controller performance is tested experimentally for three objective functions: recovery of mean‐base pressure set‐point after perturbation; and minimization of drag or wake fluctuation intensity. An adaptive learning strategy is implemented to adjust the model to new Reynolds number ( Re ) conditions without user intervention, thereby extending the controller performance and achieving more robust control. The identified minimum drag and wake fluctuation cases are analysed using velocity field data measured with particle image velocimetry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it