Efficient strength optimization of variable stiffness laminates using lamination parameters with global failure index

Journal Article (2022)
Author(s)

Zhi Hong (Hangzhou Dianzi University)

D.M.J. Daniël (TU Delft - Aerospace Structures & Computational Mechanics)

Yujie Guo (Nanjing University of Aeronautics and Astronautics)

Research Group
Aerospace Structures & Computational Mechanics
Copyright
© 2022 Zhi Hong, D.M.J. Peeters, Yujie Guo
DOI related publication
https://doi.org/10.1016/j.compstruc.2022.106856
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Zhi Hong, D.M.J. Peeters, Yujie Guo
Research Group
Aerospace Structures & Computational Mechanics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Volume number
271
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Abstract

A computationally-efficient strength optimization method tailoring novel composite laminates using lamination parameters is developed. The method adopts a global p-norm approach to aggregate local failure indices into a global failure index, based on the Tsai-Wu failure criterion. For design purposes, the novel composite laminates are characterized via lamination parameters that can subsequently be transformed into locally variable fiber orientations in an existing three-step optimization method. An elliptical formulation of the conservative failure envelope is applied to represent the Tsai-Wu criterion in terms of lamination parameters. A lamination-parameter-based two-level approximation for the global failure index is derived, which guarantees the anticipated conservativeness and convexity in a gradient-based optimization framework. Numerical results show that the computational efficiency of the proposed strength optimization method improves remarkably with a proper value of p, compared to the existing local-based min-max method. The method is also shown to be robust and generate converged optimum designs even in the presence of stress concentrations and singularities.

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