This thesis aims to study the design and optimization of composite wing components for aircraft, focusing on the pivotal goal of reducing weight while preserving and enhancing structural performance. This is one of the goals of the COST Action joined in the span of the thesis: CA18203 - Optimising Design for Inspection (ODIN). Within this COST Action, a comparison between different Finite Element modeling predictions of the response of this structure subjected to 4-point loading has been presented
by Bisagni et al. [1] before this thesis. The thesis, which corresponds to the optimization of this same structure, is the continuity of this work. The foundation of this thesis is built upon a literature study that investigates the optimization of composite wing structures. It explores diverse optimization formulations, including design variables, objectives, and constraints. Different methodologies for optimizing wing structures, like multi-objective and probabilistic methods, are studied. Various algorithms used to tackle these challenges are presented, shedding light on their advantages and drawbacks. Once a solid knowledge of wing structure optimization is built, preliminary analyses (study of coupon geometries, failure criteria, and the buckling behavior of various materials and geometries) form the basis for the upcoming optimization strategy. These preliminary investigations refine our understanding and guide the selection of optimal constraints, such as failure criteria. Based on these preliminary analyses and the literature study, the optimization strategy has been established. A new configuration with cut-outs is developed to explore new innovative designs. To solve the optimization problem, Genetic Algorithms, inspired by real-life behaviors and processes, emerge as the optimization algorithm of choice. These algorithms contribute to the development of design solutions that are better than traditional designs. The objective is to achieve weight reduction in the structure while considering its structural performance. Consequently, the fitness function, employed to assess candidate designs within the Genetic Algorithm, is based on three primary factors: weight, stiffness, and buckling behavior. Weight carries the most significant part of the fitness function, accounting for approximately 80% of the total value, whereas the remaining two factors contribute approximately 10% each. To evaluate these aspects, weight is determined through basic calculations, while stiffness and buckling behavior are assessed by simulating a 4-point bending test using Abaqus.
The implementation of the Genetic Algorithm, adapted to our special case, allows us to showcase its effectiveness in achieving optimal designs. These optimal designs achieve remarkable weight reductions while maintaining great structural performance. Following 20 generations, each comprising 10 potential candidates, one of the two termination criteria is triggered: there is no improvement in the best solution for eight consecutive generations. The optimized solution obtained is 16.89 kg lighter compared to the baseline configuration studied by Bisagni et al. [1]. This represents a significant 25% reduction in structure weight. Concerning the structural performance, only a reduction of 1.5% and 0.25% is observed for the stiffness and buckling performance, respectively, which is acceptable regarding the weight reduction. Nonetheless, achieving such a reduction in wing weight may not be realistic as it was specifically tailored for the test case of this thesis, other loading scenarios need to be studied to assess its applicability.
These outcomes underscore the potential of optimization techniques in aerospace engineering, paving the way for the development of lightweight and high-performing composite wing components.