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Parameter optimisation of a carrier-based UAV drawbar based on strain fatigue analysis

Published online by Cambridge University Press:  11 February 2021

H. Chen Open the ORCID record for H. Chen [Opens in a new window] ,
X. Fang ,
Z. Zhang ,
X. Xie ,
H. Nie  and
X. Wei
H. Chen
Affiliation:
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
X. Fang
Affiliation:
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
Z. Zhang
Affiliation:
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
X. Xie
Affiliation:
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
H. Nie*
Affiliation:
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
X. Wei
Affiliation:
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing, Jiangsu, China
*
hnie@nuaa.edu.cn
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Abstract

Carrier-based unmanned aerial aircraft (UAV) structure is subjected to severe tensile load during takeoff, especially the drawbar, which affects its fatigue performance and structural safety. However, the complex structural features pose great challenges for the engineering design. Considering this situation, a structural design, fatigue analysis, and parameters optimisation joint working platform are urgently needed to solve this problem. In this study, numerical analysis of strain fatigue is carried out based on the laboratory fatigue failure of the carrier-based aircraft drawbar. Taking the sensitivity of drawbar parameters to stress and life into account and optimum design of drawbar with fatigue life as a target using the parametric method, this study also includes cutting-edge parameters of milling cutters, structural details of the drawbar and so on. Then an experimental design is applied using the Latin hypercube sampling method, and a surrogate model based on RBF neural network is established. Lastly, a multi-island genetic algorithm is introduced for optimisation. The results show that the error between the obtained optimal solution and simulation is 0.26%, while the optimised stress level is reduced by 15.7%, and the life of the drawbar is increased by 122%.

Keywords

carrier-based UAVstrain fatiguesurrogate modelstructural optimisation
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1288 , June 2021 , pp. 1083 - 1102
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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