13:30   Aeroelastic optimisation 3 Chair: Héctor Climent 13:30 30 mins Aeroelastic reliability based optimisation of composite plates via surrogate modeling Roger Ballester Claret, Nicolo Fabbiane, Christian Fagiano, Cédric Julien, Didier Lucor Abstract: In aerospace engineering, optimizing composite laminates in aeronautical structures can be a complex problem which often results in intricate models that are computationally demanding. Addressing this, our work introduces a reduced model approach, employing a composite plate as a representative element of an aircraft wing [1]. The primary objective is to enhance aeroelastic behaviour in response to wind gusts, focusing on minimizing the bending moment generated by such gusts. Concurrently, flutter analysis is conducted, serving as the primary constraint in our optimization problem. The optimization of composite materials presents unique challenges, particularly due to the non-convex nature of the stacking sequence design space and the presence of numerous local minima. Furthermore, the variable number of design parameters (angles of plies) adds to the complexity given that the possible stacking error must be considered within the optimisation process [2]. To navigate these challenges, our approach utilizes material homogenization, transforming the problem into one within a convex design space with a fixed number of variables. Building upon this foundation, we implement a surrogate model-based optimization strategy. The surrogate models, are developed within the homogenized design space using constrained Efficient Global Optimization (C-EGO) [3]. Following the establishment of these models, a Genetic Algorithm (GA) is employed to determine the optimal stacking sequence [4]. The genetic algorithm attempts to find a stacking sequence that optimises the aeroelastic problem with a reliability-based approach. Considering the possible uncertainties in the ply orientation of the composite laminate the GA manages to tailor the composite to the aeroelastic constraints. The results of this two-step optimization process, combined with the proposed dual design space approach, demonstrate the efficacy of surrogate models in exploring the full spectrum of optimal composite configurations. Moreover, the coupling of this methodology with the aeroelastic problem at hand permits for a cost-efficient optimisation reducing notably the number of calls to the aeroelastic solver compared to gradient based optimisation methods. This methodology not only streamlines the design process for composite aeroelastic structures but also showcases its possible applicability in more complex aeroelastic scenarios. 14:00 30 mins Unified model of sandwich panel core and faces for aeroelastic optimization Vladimír Hostinský, Jurij Sodja, Ivo Jebáček, Jan Navrátil Abstract: Metamaterials show a considerable potential in the field of complex optimization of aerospace structures. To fully exploit this potential, the authors present a novel approach to modeling the structural response of a sandwich panel with metamaterial core and CFRP skins that could be used within a single-step optimization process. The proposed approach is illustrated using a model of a panel with aluminum honeycomb core. Obtained results confirm the high potential of the inclusion of the core optimization into the optimization framework. Simple preliminary validation utilizing the finite element analysis presented in the closure of this study yielded a satisfactory agreement with the results of the proposed analytical model. The maximum reached difference of five percent can be attributed to a different shape of deformation of the honeycomb core within the sandwich as opposed to a deformation of the honeycomb core alone. 14:30 30 mins A Panel-Free Aeroelastic Solver with Adjoint Sensitivities Hollis Smith, Joshua Deaton Abstract: The conventional approach to aeroelastic analysis with a panel method involves constructing a panel mesh of external surfaces and their wakes to estimate lifting pressures, constructing a finite-element mesh of the substructure to estimate deformations, and implementing a consistent and conservative interpolation scheme to simultaneously resolve the coupled state equations. Both the structural and the aero meshes are typically constructed from an explicit geometry that does not readily accommodate topological changes. When used in an optimization, the conventional approach requires an expensive re-meshing step for both analyses upon changes in the design, and expensive re-evaluation of topological operations like intersections, unions and subtractions. In recent years, feature-mapping methods have been applied to solve structural displacements by mapping the geometric components in the design to an implicit field-representation (circumventing the need to re-mesh upon design changes). The implicit representation naturally and robustly transforms topological operations into inexpensive arithmetic operations. In contrast to conventional free-form topology optimization methods, the feature-based approach parameterizes the design in terms of high-level geometric features, resulting in optimized designs that are directly compatible with existing parametric CAD systems. We present a novel panel-free approach to solve potential-based lifting pressures that is inspired by the feature-mapping approach used in structural topology optimization. The lifting surfaces and their wakes are mapped to a fixed analysis domain wherein an efficient multi-grid solver resolves the lifting pressures. In addition, we present a natural method to consistently and conservatively transfer loads and displacements between disciplines. Since all of the mappings are differentiable, we present an algorithm to adjointly compute the design-sensitivity of quantities depending on the coupled aeroelastic state, facilitating efficient gradient-based optimization. The computational efficiency gained by circumventing the need to re-build and re-mesh the explicit geometry for each analysis, the topological design flexibility gained by mapping to an implicit representation, and the efficient adjoint computation of design-sensitivities make the proposed aeroelastic solver an attractive alternative to the conventional approach.

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