Structural optimization of composite UAV wings

• Autor(a) principal: Silva, Filipe Miguel Jesus
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.6/13020
• topic: Composites; Finite Element Analysis; Genetic Algorithm; Integer Optimization; Optimization; Stacking Sequence Table; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Aeronáutica

Mass is a key factor during the design of an aircraft. Since the wing is one of the heaviest components, an accurate prediction of its mass is essential for a correct definition of its size and evaluation of the aircraft’s performance. Composite materials usually compose aircraft structures due to their high specific strength and ease of manufacture. By finding the optimal stacking sequence and layer orientation, it is possible to minimize the mass of components ensuring that constraints such as structural integrity and maximum displacement are fulfilled. Many studies show that Evolutionary Algorithms combined with Finite Element Analysis is a good approach to optimize composite structures regarding layer composition and orientation. This thesis describes the development of a tool that performs the structural optimization of Unmanned Aerial Vehicles’ wings made of composite structures. The motivation for this work lies in the need to improve the structural sizing method used by the University of Beira Interior teams during the design phase for the Air Cargo Challenge competition. Typically, the wing is a two-cell beam structure with sandwich skin, spar webs and laminated spar caps. In the computational model, triangular plate elements are used to represent both spar webs and the skin, and bar elements define the spar caps. For this structure, the stacking sequence of the skin must be found as well as the number of layers of each spar cap for minimum mass subject to failure and deflection (wing tip deflection and twist) constraints. To generate the mesh, the wings’ cross-section is divided into five sub-sections: leading-edge, upper and lower surfaces between spar positions and leading and rear spar webs. Based on the number of divisions and the spacing technique chosen for each sub-section, the section nodes for the structural problem are computed. Since the number of divisions and spacing technique of the cross-section are kept constant across the span, the panel nodes are the result of the interpolation of the section nodes between the extreme sections of each panel. After the node numbering process, the mesh is defined panel-by-panel, generating triangular elements for the panel skin and webs of both spars and linear elements for the spar caps. The loads are computed using the lifting line theory and transferred to the mesh considering that they are applied on the web and caps of the main spar. The solution uses MYSTRAN as the finite element solver to assess failure criteria, and the Simple Genetic Algorithm from OpenMDAO to solve the integer optimization problem. Typically, the materials considered for the problem are orthotropic, including unidirectional and bidirectional fabrics. Manufacturing constraints are addressed considering symmetrical sandwich structures, in which the core is the central layer, and by orienting the unidirectional fabric of the spar caps with the longitudinal direction of the panel. The maximum number of layers for each structure and the orientations in which a fabric can be applied must be specified by the user. To reduce the number of design variables, a database containing all possible arrangements of layer’s material and orientation for each structure is generated. This method allows to fix the number of design variables per panel to seven: stacking sequences of shell, main and rear spar webs, and number of layers of each spar cap. Since this is a high demanding computational optimization process, the parallel processing option available at OpenMDAO is activated, allowing to simultaneously analyze more than one individual of a generation. As a case study, the central panel of the 2019 Air Cargo Challenge aircraft wing from AERO@UBI is optimized, testing the optimization tool. A reduction of 16.5% on the panel’s mass is achieved during the simulations. From the different optimization settings tested it is considered that a mutation rate of 0.05, population size of 30 individuals and 20 generations is the combination that best suit this optimization problem. From the results obtained, it is recommended to implement an additional constraint able of measuring the difference between the deformed and the original shapes to prevent excessive aerodynamic losses.