Abstract

Thin-walled elements are widely utilized in the design of aerospace structures as they allow to obtain lightweight structures. However, while the structures so designed may be sufficient to carry the in-plane tensile loads and satisfy strength requirements, they are often prone to fail under buckling induced by compressive or shear loads. Buckling is a highly non-linear phenomenon caused by the sudden conversion of a large amount of in-plane strain energy into bending strain energy [1]. This process may be expressed in different modes. For example, global buckling is said the catastrophic collapse of the entire structure; local buckling affects a portion of the skin; stiffener buckling occurs in correspondence of stiffener segments. Other “special” buckling modes are typical of sandwich structures (wrinkling, dimpling, etc.). In order to guarantee structural safety against buckling, reinforcing elements (stiffeners) running in the longitudinal and transverse directions are added to the panel skin to increase the stiffness of the structure. The proper selection of the stiffening configuration and materials leads to minimize structural weight thus maximizing payload. A number of trade studies on the sensitivity of structural weight of stiffened panels to parameters such as stiffener geometry, materials, and type of construction and manufacturing methods were presented in literature (see for example, [2,3]). Design concepts are usually compared and preliminarily selected to be further developed later on the basis of their structural weight (typically, the weight per unit area) which is minimized by means of optimization methods. Structural optimization is mandatory in the design of aerospace structures. Design variables are repeatedly perturbed to satisfy non-linear constraints on displacements, stresses and critical buckling loads. Optimization methods can be divided in two main groups: Approximate Optimization Methods (AOM) and Global Optimization Methods (GOM). AOM formulate and solve a set of sub-problems where the original non-linear functions of the optimization problem are replaced by linear, quadratic or higher order approximations built including gradient information. Larger fractions of design space can be explored using multi-start approximate optimization (MSAO) where different optimization runs are performed starting from points generated randomly. The main difficulty of approximate optimization is to keep the quality of the approximation as highest as possible and always reliable in the region of design space currently being searched. Furthermore, approximate models should change during the optimization process based on the sequence in which design constraints become active. Global optimization methods search the optimum design by generating randomly a certain number of trial designs. This is done in purpose to expand the portion of design space explored by the optimizer thus increasing the probability of finding the global optimum

Autore Pugliese

• Casavola Caterina , Lamberti Luciano , Pappalettere Carmine

Tutti gli autori

• Casavola C , Lamberti L , Pappalettere C

Titolo volume/Rivista

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Anno di pubblicazione

2012

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Settori ERC

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Codici ASJC

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