Deep reinforcement learning-based active flow control for a tall building
Bibliographic record
Abstract
This study focuses on developing a deep reinforcement learning (DRL) flow control policy to mitigate aerodynamic loads of a tall building at high Reynolds number Re=7.53×104. Multiple jets are placed at the four corners and the free end of the building, aiming to suppress wind loading of the building under varying incoming wind conditions. Pressure probes on the building surface are used as feedback observers. The soft actor-critic (SAC) algorithm is deployed to train an effective DRL control policy. The DRL agent can optimize the jet velocities, resulting in reductions of 39.1%, 53.7%, and 38.4% in the fluctuations of the drag, lift, and moment coefficients, respectively. Furthermore, the mean drag coefficient is reduced by 27.3%. This study investigates the behavior of multiple jets and their effects on the wind force and flow field. It was found that the multiple jets can reduce crosswind force fluctuations in tall buildings, enhance downwash flow, and mitigate the shedding of wake vortices. These results highlight the potential of DRL in active flow control and lay the foundation for the efficient, robust, and practical implementation of this control technique in real-world engineering applications.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".