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Multi-scale optimisation of thin-walled structures by considering a global/local modelling approach

Abstract : In this work, a design strategy for optimising thin-walled structures based on a global-local finite element (FE) modelling approach is presented. The preliminary design of thin-walled structures can be stated in the form of a constrained non-linear programming problem (CNLPP) involving requirements of different nature intervening at the different scales of the structure. The proposed multi-scale optimisation (MSO) strategy is characterised by two main features. Firstly, the CNLPP is formulated in the most general sense by including all design variables involved at each pertinent scale of the problem. Secondly, two scales (with the related design requirements) are considered: (a) the structure macroscopic scale, where low-fidelity FE models are used and (b) the structure mesoscopic scale (or component level), where more accurate FE models are involved. In particular, the mechanical responses of the structure are evaluated at both global and local scales, avoiding the use of approximated analytical methods. The MSO is here applied to the least-weight design of an aluminium fuselage barrel of a wide-body aircraft. Fully parametric global and local FE models are interfaced with an in-house metaheuristic algorithm. Refined local FE models are created only for critical regions of the structure, automatically detected during the global analysis, and linked to the global one, thanks to the implementation of a sub-modelling approach. The whole process is completely automated, and once set, it does not need any further user intervention.
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Submitted on : Tuesday, September 22, 2020 - 1:42:13 PM
Last modification on : Wednesday, October 14, 2020 - 4:08:53 AM

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Michele Izzi, Marco Montemurro, Anita Catapano, Daniele Fanteria, Jérôme Pailhes. Multi-scale optimisation of thin-walled structures by considering a global/local modelling approach. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2020, ⟨10.1177/0954410020939338⟩. ⟨hal-02910815⟩

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