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	A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm
	Isakhani, Hamid; Xiong, Caihua; Yue, Shigang; Chen, Wenbin
通讯作者	Isakhani, H (corresponding author), Univ Lincoln, Sch Comp Sci, Computat Intelligence Lab CIL, Lincoln LN6 7TS, England.
会议名称	17th International Conference on Ubiquitous Robots (UR)
会议日期	JUN 22-26, 2020
会议地点	Kyoto, JAPAN
英文摘要	Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. However, primitive nature of the bioinspired research impedes such achievements, hence to bypass these limitations, this study introduces a bioinspired non-cooperative multiple objective optimization methodology based on a novel fusion of PARSEC, Nash strategy, and genetic algorithms to achieve insect-level aerodynamic efficiencies. The proposed technique is validated on a conventional airfoil as well as the wing cross-section of a desert locust (Schistocerca gregaria) at low Reynolds number, and we have recorded a 77% improvement in its gliding ratio.
来源出版物	2020 17TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS (UR)
ISSN	2325-033X
出版年	2020
页码	506-513
ISBN	978-1-7281-5715-3
出版者	IEEE
类型	Proceedings Paper
语种	英语
开放获取类型	Green Submitted, Green Published
收录类别	CPCI-S
WOS记录号	WOS:000612835600081
WOS关键词	DESERT LOCUST ; FLIGHT ; PERFORMANCE ; LIFT
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics
WOS研究方向	Computer Science ; Engineering ; Robotics
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353044
作者单位	[Isakhani, Hamid; Yue, Shigang] Univ Lincoln, Sch Comp Sci, Computat Intelligence Lab CIL, Lincoln LN6 7TS, England; [Xiong, Caihua; Chen, Wenbin] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Isakhani, Hamid,Xiong, Caihua,Yue, Shigang,et al. A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm[C]:IEEE,2020:506-513.