4.7 Article

A multi-scale two-level optimisation strategy integrating a global/local modelling approach for composite structures

期刊

COMPOSITE STRUCTURES
卷 237, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compstruct.2020.111908

关键词

Preliminary design; Optimisation; Global/local modelling approach; Composite material; Stiffened panel; Fuselage

资金

  1. European Union under the Horizon 2020 Research and Innovation Program [723149]

向作者/读者索取更多资源

In this work, a mull-scale optimisation strategy for the preliminary design of composite structures involving design requirements at different scales, is presented. Such a strategy, denoted as GL-MS2LOS, has been formulated by integrating a dedicated global-local (GL) modelling approach into the mull-scale two-level optimisation strategy (MS2LOS). The GL-MS2LOS aims at proposing a very general formulation of the design problem, without introducing simplifying hypotheses and by considering, as design variables, the full set of geometric and mechanical parameters defining the behaviour of the composite structure at each pertinent scale. By employing a GL modelling approach, most of the limitations of well-established design strategies based on analytical or semi-empirical models are overcome. The effectiveness of the presented GL-MS2LOS is proven on a meaningful study case: the least-weight design of a composite fuselage barrel of a wide-body aircraft undergoing various loading conditions and subject to requirements of different nature. Fully parametric global and local FE models are interfaced with an in-house metaheuristic algorithm to perform the optimisation. 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 general nature of the GL-MS2LOS allows finding an optimised configuration characterised by a weight saving of 40% when compared to an optimised aluminium solution obtained through a similar GL optimisation strategy.

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