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A CFD data-driven aerodynamic model for fast and precise prediction of flapping aerodynamics in various flight velocities

Published online by Cambridge University Press:  31 March 2021

Xuefei Cai
Affiliation:
Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTU-CU ICRC), Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai200240, PR China Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba263-8522, Japan
Dmitry Kolomenskiy
Affiliation:
Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba263-8522, Japan Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo152-8550, Japan
Toshiyuki Nakata
Affiliation:
Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba263-8522, Japan
Hao Liu*
Affiliation:
Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTU-CU ICRC), Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai200240, PR China Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba263-8522, Japan
*
Email address for correspondence: [email protected]

Abstract

Precise prediction of unsteady flapping aerodynamics in insect flight is of potential importance in the analysis of maneuverability and flight control. While the quasi-steady model is a cheap while reasonable tool, accurate evaluation of unsteady dynamic effects in complex flight behaviours remains a challenge. Here we develop a computational fluid dynamics (CFD) data-driven aerodynamic model (CDAM), which is informed by high-fidelity CFD simulations using overset meshes to enable the precise and fast prediction of both cycle-averaged and transient aerodynamic force, torque and power with various flying motions and wing kinematics. The CDAM comprises a quasi-steady model for flapping wings and an aerodynamic model for a moving body. The least square method and a surrogate method are employed to achieve aerodynamic coefficient fitting through training using a CFD database. With comparison to CFD test data, the CDAM is validated to be capable of accurately evaluating the aerodynamic force, torque and power of a wing-body bumblebee model in various flight velocities. A genetic optimization algorithm embedded with CDAM is proposed to determine trimmed states for forward flight through adjusting wing kinematics, indicating that bumblebees likely fly in a minimized mass-specific aerodynamic power consumption. The CDAM is further applied to proportional-derivative-based longitudinal flight control of bumblebee hovering, with the control parameters optimized by Laplace transformation and the root locus method, which is implemented consistently in both CDAM and CFD environments. Our results demonstrate that CDAM provides a versatile tool to achieve fast and precise aerodynamical prediction for flying insects in various flight behaviours.

Type
JFM Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Supplementary data (Bumblebee)

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Supplementary data (Hawkmoth)

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