Optimal Hovering Wing Kinematics of Flapping-Wing Model Using Reinforcement Learning
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yang, Hyeon-Ho | ko |
| dc.contributor.author | Lee, Sang-Gil | ko |
| dc.contributor.author | Han, Yu-Jeong | ko |
| dc.contributor.author | Han, Jae-Hung | ko |
| dc.date.accessioned | 2023-01-05T08:02:46Z | - |
| dc.date.available | 2023-01-05T08:02:46Z | - |
| dc.date.created | 2023-01-02 | - |
| dc.date.created | 2023-01-02 | - |
| dc.date.issued | 2022-09-07 | - |
| dc.identifier.citation | 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/304054 | - |
| dc.description.abstract | The unsteady vortex method is modified to estimate the contribution of leading-edge vortices and was used to simulate the unsteady aerodynamics of the flapping wing model. The reinforcement learning environment to train flapping wing kinematics is established based on a deep neural network. The optimal hovering wing kinematics that leads to maximum lift and lift/drag ratio is found. | - |
| dc.language | English | - |
| dc.publisher | International Council of the Aeronautical Sciences | - |
| dc.title | Optimal Hovering Wing Kinematics of Flapping-Wing Model Using Reinforcement Learning | - |
| dc.type | Conference | - |
| dc.identifier.scopusid | 2-s2.0-85159687958 | - |
| dc.type.rims | CONF | - |
| dc.citation.publicationname | 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022 | - |
| dc.identifier.conferencecountry | SW | - |
| dc.identifier.conferencelocation | Stockholm | - |
| dc.contributor.localauthor | Han, Jae-Hung | - |
| dc.contributor.nonIdAuthor | Han, Yu-Jeong | - |
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