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ORIGINAL ARTICLE

An enhanced reduced-order model based optimization method for buckling and postbuckling performances of variable stiffness composite plates

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Pages 4044-4065 | Received 09 Feb 2021, Accepted 12 Apr 2021, Published online: 24 May 2021
 

Abstract

The great potential of variable stiffness laminates in maximizing buckling load is obvious over straight fiber laminates, however the optimum design of such VAT laminates which has much more design variables and larger computational efforts, is still a challenge even for the modern computational method and capability. In addition, the postbuckling performance and the correlations with buckling resistance still need to be further studied for VAT laminates. This work proposes an enhanced reduced-order model based on the improved Koiter perturbation theory for buckling and postbuckling analysis of variable stiffness composite plates. The VAT laminas composed by “two-segment” fibers and “four-segment” fibers are considered, respectively, in construction of the reduced-order finite element model. The nonlinear prediction for initial postbuckling response of VAT laminate can be achieved, by solving the small-scaled reduced-order model. The nonlinear buckling load, postbuckling stiffness and postbuckling stiffness residual factor, are accurately and efficiently extracted from the initial postbuckling predictor, and then subsequently to be selected as inputs to the GA based optimization process for the design of VAT laminates. Extensive numerical investigations validate that the proposed method can find a combination of several varied angle fiber layers with constant thicknesses such that the combination results in an optimal stiffness distribution and provides a more favorable structural performance in both buckling and postbuckling regime for different demands of structure design.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11972300 and 11602286) and the Fundamental Research Funds for the Central Universities of China (Grant No. G2019KY05203).

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