Optimization of the Unmanned Aerial Vehicle Composite Wing Lightweight Design Based on the Particle Swarm Algorithm

Аuthors

Abdullin I. N.^*, Chen F. ^**, Tarantaeva A. R.^***

Kazan National Research Technical University named after A.N. Tupolev, Kazan, Russia

*e-mail: ilfir528@mail.ru
**e-mail: 1609944784chen@gmail.com
***e-mail: aygultarantaeva@yandex.ru

Abstract

In the framework of the unmanned aerial vehicle (UAV) structure optimization the presented article proposes the optimization design method for the wing structure based on the particles swarm algorithm. At the first stage, the initial layout of the structure is determined. Then the space for optimization is being identified through the simulation analysis. Multi-level optimization of the structure is being performed at the final stage.
The relevance of the study is stipulated by the need to optimize the wing designs of unmanned aerial vehicles for the weight reduction while maintaining the required strength and rigidity.
The research objectives are as follows: analyzing the strength of the wing structure by the maximum stress criteria and the Hashin criterion; evaluating the structure stress-strain state; developing a technique for the wing geometric parameters optimizing, and verifying the effectiveness of the proposed technique.
The scientific novelty of the study consists in developing a comprehensive technique for the strength assessing of a composite wing by the modern fracture criteria and the proposal of a design optimization technique based on the particle swarm algorithm employing a surrogate model.
The wing structure was determined based on the requirements for the unmanned aerial vehicle design and the wing main technical parameters. Boundary conditions for the wing structural analysis were assigned with account for the aerodynamic loads on the wing distribution. 
The authors considered the wing structure, implemented according to the two-spar multi-rib type scheme, including skin, spars and ribs, was considered, and performed the analysis of the external loads distribution on the wing. To determine accurately the state of the wing structure destruction, the maximum stresses and the Hashin fracture criterion were used in this work as evaluation criteria. A conclusion was draw that the original wing design meets safety requirements, but has significant potential for optimization based on the analysis of three aspects. The thicknesses of the wing structural elements were selected as optimization design parameters.
This article realizes a multi-level optimization design of the UAV wing structure based on the particle swarm algorithm. The methodology includes the integration of a surrogate model and an algorithm to optimize the dimensional parameters of the wing structure. The thicknesses of the wing structural elements is selected as the optimization design parameters, and the optimization objective consists in minimizing the total wing mass.
A comparison of the characteristics prior to and after optimization revealed the following: the mass of the wing structure decreased by 18.1%, which confirms the effectiveness of lightening; the stress index and fracture coefficient increased, but remained below the preset limit of 0.8. The maximum stress zones are still localized near the front and middle spars in the root part; the maximum displacement of the structure increased by 15.27%, but it remained well below the acceptable value for rigidity requirements. The surrogate model data and numerical modeling results comparison revealed that the error of all indicators does not exceed 8%, which confirms the model high accuracy and the optimized results reliability.
The developed technique allows optimizing the UAV wings design, reducing thereby the structure weight while sustaining strength, when accounting for the features of the structural elements made of composite materials.

Keywords:

optimization of composite wing design, multi-criteria optimization, Particle Swarm Optimization (PSO), Radial Basis Function (RBF), the Kriging model, Response surface methodology (RSM), radial basis functions

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