Efficient collective swimming by harnessing vortices through deep reinforcement learning
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- Siddhartha Verma
- Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
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- Guido Novati
- Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
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- Petros Koumoutsakos
- Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
説明
<jats:title>Significance</jats:title> <jats:p>Can fish reduce their energy expenditure by schooling? We answer affirmatively this longstanding question by combining state-of-the-art direct numerical simulations of the 3D Navier–Stokes equations with reinforcement learning, using recurrent neural networks with long short-term memory cells to account for the unsteadiness of the flow field. Surprisingly, we find that swimming behind a leader is not always associated with energetic benefits for the follower. In turn, we demonstrate that fish can improve their sustained propulsive efficiency by placing themselves at appropriate locations in the wake of other swimmers and intercepting their wake vortices judiciously. The results show that autonomous, “smart” swimmers may exploit unsteady flow fields to reap substantial energetic benefits and have promising implications for robotic swarms.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 115 (23), 5849-5854, 2018-05-21
Proceedings of the National Academy of Sciences
