Deep reinforcement learning for propulsive performance of a flapping foil
Bibliographic record
Abstract
While it is challenging for a traditional propulsor to achieve a wide range of force profile manipulation and propulsion efficiency, nature provides a solution for a flapping foil such as that found in birds and turtles. In this paper, we introduce a deep reinforcement learning (DRL) algorithm with great potential for solving nonlinear systems during the simulation to achieve a self-learning posture adjustment for a flapping foil to effectively improve its thrust performance. With DRL, a brute-force search is first carried out to provide intuition about the optimal trajectories of the foil and also a database for the following case studies. We implement an episodic training strategy for intelligent agent learning using the DRL algorithm. To address a slow data generation issue in the computational fluid dynamics simulation, we introduce a multi-environment technique to accelerate data exchange between the environment and the agent. This method is capable of adaptively and automatically performing an optimal foil path planning to generate the maximum thrust under various scenarios and can even outperform the optimal cases designed by users. Numerical results demonstrate how the proposed DRL is powerful to achieve optimization and has great potential to solve a more complex problem in the field of fluid mechanics beyond human predictability.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".