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Erschienen in: Structural and Multidisciplinary Optimization 5/2019

04.07.2019 | Research Paper

An investigation on design of signs in composite laminates to control bending-twisting coupling effects using sign optimization algorithm

verfasst von: Zhao Jing, Jianqiao Chen

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 5/2019

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Abstract

A sign optimization algorithm (SOA) is proposed to design the “±” signs in composite laminates to control the bending-twisting coupling effects. Owing to that the bending-twisting coupling stiffness are cubic on thickness, the innovation is to design the signs of ply orientations from the mid-plane to the outermost sequentially and iteratively. In this manner, the nondimensional anisotropic coefficients are controlled to the target values. Numerical examples are adopted to verify the effectiveness and efficiency of SOA. First, the signs of symmetric laminates [θ32]s are optimized with various boundaries, load ratios, and aspect ratios to show the bending-twisting coupling effects on bending, buckling, and vibration responses of composite plates. Second, the bending-twisting coupling effects are minimized since they may cause large errors in buckling load prediction when using closed-form solution after neglecting them. Third, the optimal sequences obtained from heuristic algorithms are employed for sign optimization. Results show that the bending-twisting coupling effects cannot be neglected; moreover, the buckling and vibration performances can be further improved by redesigning “±” signs in composite laminates. This research aims to provide a design technique to minimize the error induced by bending-twisting coupling and increase the probability to find the global optimum.

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Metadaten
Titel
An investigation on design of signs in composite laminates to control bending-twisting coupling effects using sign optimization algorithm
verfasst von
Zhao Jing
Jianqiao Chen
Publikationsdatum
04.07.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 5/2019
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-019-02315-6

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