This work presents an efficient methodology for the optimum design of functionally graded structures using a Krigingbased approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process. In this approach, the surrogate model is used to replace the high-fidelity structural responses obtained by a NURBS-based isogeometric analysis. In addition, the impact of key factors on surrogate modelling, as the correlation function, the infill criterion used to update the surrogate model, and the constraint handling is assessed for accuracy, efficiency, and robustness. The design variables are related to the volume fraction distribution and the thickness. Displacement, fundamental frequency, buckling load, mass, and ceramic volume fraction are used as objective functions or constraints. The effectiveness and accuracy of the proposed algorithm are illustrated through a set of numerical examples. Results show a significant reduction in the computational effort over the conventional approach.
Use of fiber-reinforced laminated composites has proved itself as a valuable option in the manufacturing of risers, particularly for deepwater applications, a scenario where its lightweight related properties and good fatigue resistance are most needed. In addition, its use allows these structures to be tailored to meet specific manufacturing, safety, and stability criteria. This paper proposes an optimization model to composite risers in a free-hanging catenary configuration that considers multiple load cases and two objective functions. The optimization is carried out using a modified version of the Nondominated Sorting Genetic Algorithm II (NSGA-II). The riser structural analysis is performed by an inextensible cable model that accounts for the vertical static loads, floater offset and current loads in a fast and efficient way. The proposed algorithm is validated using a benchmark problem and applied to obtain the Pareto Front of a composite riser.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.