Research on Autonomous Airfoil Stall Suppression System Utilizing Machine Learning
| Project/Area Number |
17H03476
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Aerospace engineering
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Rinoie Kenichi 東京大学, 大学院工学系研究科(工学部), 教授 (20175037)
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| Co-Investigator(Kenkyū-buntansha) |
今村 太郎 東京大学, 大学院工学系研究科(工学部), 准教授 (30371115)
砂田 保人 東京大学, 大学院工学系研究科(工学部), 助教 (50216488)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥13,780,000 (Direct Cost: ¥10,600,000、Indirect Cost: ¥3,180,000)
Fiscal Year 2020: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2019: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2018: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2017: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Keywords | 航空宇宙工学 / 流体工学 / 剥離流 / 流体制御 |
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| Outline of Final Research Achievements |
In this research, we will utilize the machine learning technique, which is an artificial intelligence method, to control the flow field around the aircraft, specifically, to control the airfoil stall suppression. So far, in order to attain stall suppression, we have developed a control device using a bubble burst control plate, and by using that device, we succeeded in suppressing stall in response to changes in the airfoil angle of attack. However, in order to operate this system, it was necessary to input the airfoil stall conditions into the system in advance. Here, we constructed an airfoil stall suppression system that utilizes machine learning technique. This system detects the signs of stall by itself and autonomously and accurately suppresses stall.
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| Academic Significance and Societal Importance of the Research Achievements |
航空機事故の一つの原因として、翼の失速があげられる。この航空機の失速を防止することは、航空安全の向上につながるため、航空工学上の重要な課題であるとともに社会的意義は高い。更に本研究では機械学習を活用して翼まわりの流れの制御を行うが、この成果を活用することで、流体力学的にも、一般的な流れの制御手法の高度化につながる。
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Report
(5 results)
Research Products
(25 results)
