This study presents an imperfection-tolerant, surrogate-assisted framework for the multi-objective optimisation of variable-stiffness (VS) composite cylinders that explicitly incorporates experimentally measured geometric imperfections. Principal component analysis (PCA) is applied to extract dominant imperfection modes from experimental data, and Latin hypercube sampling (LHS) is used to generate statistically consistent synthetic fields, which are subsequently mapped onto nonlinear finite element (FE) models. Linear buckling and geometrically nonlinear collapse analyses are performed under axial compression to determine the ideal and actual load-carrying capacities, from which the knockdown factor (KDF), quantifying imperfection sensitivity, is derived. Gaussian Process Regression (GPR) surrogates are trained to predict the mass and collapse loads of perfect and imperfect geometries with high cross-validated accuracy, while KDF is computed as their ratio. The framework enables simultaneous optimisation of three objectives: mass minimisation, collapse-load maximisation, and KDF maximisation by using Bayesian Optimisation (BO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) independently. Results demonstrate that integrating experimentally informed imperfections with surrogate-based optimisation captures the key physical trends governing buckling and imperfection sensitivity, while achieving substantial computational savings relative to direct nonlinear analyses, and that both optimisers yield consistent Pareto fronts featuring smooth, manufacturable fibre trajectories that balance lightweight efficiency, strength, and robustness.

This study presents a Bayesian Optimisation (BO) framework for the mass minimisation of variable-stiffness (VS) composite cylinders under multiple buckling constraints, incorporating manufacturing limitations derived from filament winding processes. A computationally efficient single-curvature finite element model is used to evaluate the linear buckling response of multilayered shells. BO simultaneously optimises fibre paths, number of layers, and thickness distribution, achieving comparable or improved performance relative to a Genetic Algorithm (GA) while reducing simulation time by up to 70 %. Across most design loads, BO delivers structurally efficient solutions with smooth thickness transitions and local stiffness tailoring. Although GA outperformed BO in the highest load case in terms of weight and buckling capacity, BO retained competitive performance and demonstrated higher modal richness. Buckling mode analyses revealed that BO designs support mixed-mode instabilities with greater circumferential complexity, enhancing structural adaptability. In contrast, GA designs exhibited more uniform fibre paths and axial-dominated modes, reflecting conservative reinforcement strategies. These findings highlight the capability of BO to exploit complex design spaces more effectively, offering a scalable and data-efficient alternative to traditional optimisation methods. The proposed framework is particularly well suited for high-fidelity, simulation-driven design of advanced composite structures where computational cost and manufacturability are critical constraints.

Post-buckling of thin-walled aeronautical structures induces failure modes related to skin-stiffener separation that require three-dimensional large deformation analyses for an accurate numerical prediction. Conventional finite elements are capable of solving such analyses, but with high associated computational costs, being therefore more utilized for the simulation of smaller structural components, or even restricted to perform virtual testing at coupon-level models. In this paper, a geometrically nonlinear reduced-order method using a hybrid-stress solid-shell formulation is proposed for large deformation analysis of thin-walled structures. Current reduced-order methods are mainly applicable to buckling problems, considering only the out-of-plane deformation. Furthermore, existing displacement-based reduced models involve a computationally expensive fourth-order tensor obtained with the higher-order strain energy variations. Here, a reduced-order model with only one degree of freedom is constructed for both in-plane and out-of-plane large deformation problems. It is shown that, with the hybrid-stress formulation, the constructional efficiency of the reduced system is largely improved by zeroing the fourth-order strain energy variation using the two-field Hellinger–Reissner variational principle, followed by a condensation of the stress terms that lead to a third-order approximation of the equilibrium equation. The nonlinear predictor solved by the reduced-order model can be corrected when its numerical accuracy is not satisfactory during the path-following analysis. A simple plate, a honeycomb cell with negative Poisson ratio, and a swept-back wing structure; are used as numerical examples to verify that the proposed method enables a superior path-following capability for the three-dimensional analysis of thin-walled structures undergoing large deflection, large rotation and large strains. Furthermore, an experimental validation of the proposed method is presented using a variable-thickness plate with mixed composite-metallic materials, undergoing large out-of-plane deflection.

This paper investigates 3-point bending failure of five different types of GLARE laminates (2A, 2B, 3, 4A and 4B). 73 configurations (419 specimens), with different stacking sequences and aluminum layer thicknesses are tested. Failure mechanisms, effect of stacking sequence, effect of aluminum rolling direction, effect of displacement rate and energy absorption are analyzed. Configurations with predominantly 0°glass fiber layers fail with delamination as the major failure mode, while configurations with predominantly 90°glass fiber layers fail with central cracking as the major failure mode. GLARE 3, with 1:1 ratio of 0°and 90°fibers, fail with an equal mix of delamination and central cracking. A semi-analytical framework that can be used to predict the force versus displacement curve for central cracking failure is proposed and validated.

The design and optimisation of aircraft wings are critical tasks in aerospace engineering, requiring a balance between structural integrity, aerostructural performance, and manufacturability. This multifaceted challenge involves the interplay of various disciplines, each with distinct parameters and constraints. Traditional design approaches often fall short, necessitating advanced methodologies like Multidisciplinary Design Optimisation (MDO). MDO integrates aerodynamic, structural, and manufacturability analyses to explore a vast design space and identify optimal solutions that meet performance, safety, and cost criteria. Advancements in manufacturing technologies and material sciences have led to the increased use of composite materials, which offer an excellent weight-to-strength ratio. Aeroelastic Tailoring, which incorporates directional stiffness into structural design, further enhances performance. This study employs lamination parameters to efficiently represent composite layups within a gradient-based optimisation process, aiming to minimise weight while ensuring feasibility across multiple constraints. The work highlights the challenge of optimising aircraft designs using multiple models of varying fidelity. Traditional sequential optimisation approaches, which progressively integrate disciplines, may miss potential superior designs due to limited initial information. Instead, concurrent optimisation schemes are explored, utilising both low-fidelity (beam-based) and high-fidelity (shell-based) models. This approach promises structural feasibility, reduces computational costs, and incorporates high-fidelity information early in the design process. The envisioned methodology bridges different design stages, enabling better overall aircraft performance. By aligning and comparing a beam-based and shell-based model, the study explores their use in multi-fidelity optimisation. The results demonstrate the feasibility and benefits of this approach, offering a robust framework for future aircraft design projects.

This paper introduces a new computationally efficient tool to predict the post buckling behavior of thin-walled aircraft structures, particularly stiffened panels. Focusing on the critical transition where local buckling alters load distribution but retains the structure’s load-carrying capacity, the proposed postbuckling analysis method employs a semi-analytical Rayleigh-Ritz-based model and the perturbation approach. The assumed geometrically compatible displacement field functions are based on hierarchical polynomials, which are able to enhance the versatility and computational efficiency of the semi-analytical model, enabling its application across a wider range of structural configurations and boundary conditions, whereas currently available perturbation-based methodologies are limited to simple boundary conditions. The enhancement in computational efficiency, additionally, provides substantial benefits to the design and optimization processes. The obtained results for the linear case perfectly match the analytical values for buckling load. For the nonlinear case, results come in a good agreement with literature.

To design crash structures for disruptive aircraft designs, it is required to have fast and accurate methods that can predict crashworthiness of aircraft structures early in the design phase. Axial crushing is one of the key energy absorbing mechanisms during a crash event. In this study, various analytical models proposed for calculation of mean crushing force for thin-walled tubular structures are compared with a database of numerical and experimental values to ascertain their accuracy. Improvements to some of the models have also been proposed. Finally a generalized model based on the studied and improved analytical models for prediction of mean crushing force for closed section thin-walled tubular structures is introduced. The generalized model demonstrates high accuracy when compared against experimental/numerical dataset as evidenced by a high coefficient of determination (R²) value of 0.97 and can therefore be used to estimate the mean crushing force for closed-section thin-walled metallic tubular structures with various cross-sectional shapes and crushing modes early in the design phase.