Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm

Isakhani, Hamid, Yue, Shigang, Xiong, Caihua and Chen, Wenbin (2021) Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm. AIAA Journal, 59 (10). pp. 4002-4013. ISSN 0001-1452

Full content URL: https://doi.org/10.2514/1.J060298

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Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm
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Abstract

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. This study investigates the aerodynamic characteristics of an insect species called desert locust (Schistocerca gregaria) with an extraordinary gliding skills at low Reynolds number. Here, locust tandem wings are subjected to a computational fluid dynamics (CFD) simulation using 2D and 3D Navier-Stokes equations revealing fore-hindwing interactions, and the influence of their corrugations on the aerodynamic performance. Furthermore, the obtained CFD results are mathematically parameterized using PARSEC method and optimized based on a novel fusion of Genetic Algorithms and Nash game theory to achieve Nash equilibrium being the optimized wings.
It was concluded that the lift-drag (gliding) ratio of the optimized profiles were improved by at least 77% and 150% compared to the original wing and the published literature, respectively.
Ultimately, the profiles are integrated and analyzed using 3D CFD simulations that demonstrated a 14% performance improvement validating the proposed wing models for further fabrication and rapid prototyping presented in the future study.

Keywords:Nash-GA Optimisation, 3D Digital Reconstruction, Bioinspired corrugated wings
Subjects:H Engineering > H141 Fluid Mechanics
H Engineering > H400 Aerospace Engineering
H Engineering > H130 Computer-Aided Engineering
Divisions:College of Science > School of Computer Science
ID Code:47016
Deposited On:23 Nov 2021 08:53

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