Abstract-Thedesign of composite structures against buckling presents two major challenges to the designer. First, the problem of laminate stacking sequence design is discrete in nature, involving a small set of fiber orientations, which complicates the solution process. Therefore, the design of the stacking sequence is a combinatorial optimization problem which is suitable for genetic algorithms. Second, many local optima with comparable performance may be found. Most optimization algorithms find only a single optimum, while often a designer would want to obtain all the local optima with performance close to the global optimum. Genetic algorithms can easily find many near optimal solutions. However, they usually require very large computational costs. Previous work by the authors on the use of genetic algorithms for designing stiffened composite panels revealed both the above strength and weakness of the genetic algorithm. The present paper suggests several changes to the basic genetic algorithm developed previously, and demonstrates reduced computational cost and increased reliability of the algorithm due to these changes. Additionally, for a stiffened composite panel considered in this study, we present designs lighter by about 4% compared to previously obtained results.

This paper describes the application of a genetic algorithm to the stacking sequence optimization of a laminated composite plate for buckling load maximization. Two approaches for reducing the number of analyses required by the genetic algorithm are described. First, a binary tree is used to store designs, affording an efficient way to retrieve them and thereby avoid repeated analyses of designs that appeared in previous generations. Second, a local improvement scheme based on approximations in terms of lamination parameters is introduced. Two lamination parameters are sufficient to define the flexural stiffness and hence the buckling load of a balanced, symmetrically laminated plate. Results were obtained for rectangular graphite-epoxy plates under biaxial in-plane loading. The proposed improvements are shown to reduce significantly the number of analyses required for the genetic optimization.

Modem aerospace vehicle design requires the interaction of multiple disciplines, traditionally processed in a sequential order. Multidisciplinary optimization (MDO), a formal methodology for the integration of these disciplines, is evolving toward methods capable of replacing the traditional sequential methodology of aerospace vehicle design by concurrent algorithms, with both an overall gain in product performance and a decrease in design time. A parallel MDO paradigm using variable-complexity modeling and multipoint response surface approximations is presented here for the particular instance of the design of a high-speed civil transport (HSCT). This paradigm interleaves the disciplines at one level of complexity and processes them hierarchically at another level of complexity, achieving parallelism within disciplines rather than across disciplines. A master-slave paradigm manages a coarse-grained parallelism of the analysis and optimization codes required by the disciplines showing reasonable speedups and efficiencies on an Intel Paragon.

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2016-12-24T18:18:19