Rapid and optimal design of a tail-sitter VTOL ducted fan using a neural network and PSO algorithm

Document Type : Research Article

Authors

1 Shahid Beheshti University

2 Khaje Nasir Toosi University of Technology

Abstract

Considering the optimal performance and new applications of the ducted fans, especially 
in unmanned aerial vehicle missions, this paper aims to provide an optimal and rapid method for designing 
aerial vehicles based on new mathematical and analytical tools which improved and accelerated many of 
the long engineered processes. In this new fast design method, an initial design is carried out based on 
the momentum theory. Then by connecting the matrix laboratory and a ducted fan design code software, 
several optimal design schemes for the duct are extracted by the particle swarm optimization and direct 
algorithm. The parameters search domain in the algorithm is obtained from the initial design with the 
momentum theory method and the various results of optimization software, in the case. Finally, in order 
to obtain the final duct design, according to the optimized information, a multilayer perceptron neural 
network using an error backpropagation algorithm is trained which in order to obtain the optimal training 
samples and the network output validations, the neural network is trained and test by 28 airfoils sample. 
In the redesign loops, without a time-consuming optimization, the trained neural model can extract the 
duct parameters very quickly, based on the constraints of structure, control design, and mission targets. 

Keywords

Main Subjects

References

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Amirkabir Journal of Mechanical Engineering
Volume 52, Issue 12
March 2021
Pages 3353-3368

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